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African Journal of
Agricultural Research
Volume 9 Number 42
ISSN 1991-637X
16 October 2014
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African Journal of Agricultural Research
Table of Contents: Volume 9 Number 42 1 6 October, 2014
ARTICLES
Optimization of furrow irrigation systems with continuous
flow using the software applied to surface irrigation simulations - SASI
Valéria Ingrith Almeida Lima, Roberto Vieira Pordeus,
Carlos Alberto Vieira de Azevedo, Joaquim Odilon Pereira,
Vera Lúcia Antunes de Lima and Márcia Rejane de Queiroz Almeida Azevedo
Influence of physical protectors with different filters on the
initial development of Peltophorum dubium (Spreng.) Taub seedlings
Jeferson Klein, João Domingos Rodrigues, Vandeir Francisco Guimarães,
Leandro Rampim, Débora Kestring, Daniel Schwantes, Rubens Fey,
Valdemir Aleixo, Alfredo Richart, Michelli Carline Ferronato and
Augustinho Borsoi
3115
3126
Overview of soil subsurface flow in hydrological perspectives
Yinghu Zhang, Jie Yang, Haijin Zheng and Jialin Jing
3133
Efficiency impact of the agricultural sector on economic growth in Togo
Kamel HELALI, Koffi TSAGLI and Maha KALAI
3139
One and one half bound dichotomous choice contingent
valuation of consumers’ willingness-to-pay for pearl millet
products: Evidence from Eastern Kenya
Okech, S.O., Ngigi, M., Kimurto, P. K., Obare, G., Kibet, N. and Mutai B. K.
Nitrogen application and inoculation with Rhizobium tropici
on common bean in the fall/winter
Antonio Carlos Rebeschini, Rita de Cássia Lima Mazzuchelli,
Ademir Sergio Ferreira de Araujo and Fabio Fernando de Araujo
3146
3156
African Journal of Agricultural Research
Table of Contents: Volume 9 Number 42 1 6 October, 2014
Gender mainstreaming in smallholder agriculture development:
A global and African overview with emerging issues from Swaziland
Robert Mabundza, Cliff S. Dlamini and Banele Nkambule
3164
Effect of tillage system and nitrogen fertilization on organic matter
content of Nitisols in Western Ethiopia
D. Tolessa, C. C. Du Preez and G. M. Ceronio
3171
Vol. 9(42), pp. 3115-3125, 16 October, 2014
DOI: 10.5897/AJAR2014.8983
Article Number: 2B288F248063
ISSN 1991-637X
Copyright © 2014
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Full Length Research Paper
Optimization of furrow irrigation systems with
continuous flow using the software applied to surface
irrigation simulations - SASI
Valéria Ingrith Almeida Lima1, Roberto Vieira Pordeus1*, Carlos Alberto Vieira de Azevedo2,
Joaquim Odilon Pereira1, Vera Lúcia Antunes de Lima2 and
Márcia Rejane de Queiroz Almeida Azevedo 3
1
Department of Environmental Sciences, Federal Rural University of the Semi-arid, Av. Francisco Mota, 572, MossoróRN - CEP: 59.625-900, Brazil.
2
Department of Agricultural Engineering, Federal University of Campina Grande, Av. Aprígio Veloso, 882, Bodocongó,
CEP 58109970, Campina Grande -PB, Brazil.
3
Center of Agrarian and Environmental Sciences, Paraíba State University, Lagoa Seca – PB, Brazil.
Received 7 July, 2014; Accepted 18 August, 2014
Surface irrigation systems still remain the most used irrigation system worldwide mainly due to its
energy savings capacity and ease of operation. However, they show low performance level as a
consequence to the general design and inadequate management. Therefore, the aim of this study was
to develop a tool capable of optimizing the performance level of furrow irrigation systems with the
continuous flow from successive simulations of the advance phase and predictions of performance
parameters in irrigation systems. The proposed model, written in DELPHI 5.0 programming language
and called Software Applied to Surface Irrigation Simulations (SASIS) had its validation tested for
different field conditions. The results showed that the applied flow rate plays a decisive role on the
performance parameters of irrigation systems with the best performances flow rates close to the
minimum allowable levels. The field parameter that most impairs the optimization of the irrigation
performance is infiltration, while the length and slope did not decisively interfere in optimization, which
can be achieved for a wide range of values for these parameters, and also in soils with high infiltration
rates. The great difficulty in optimization is to minimize percolation losses, but in soils with low
infiltration rates, both percolation and runoff losses can be easily minimized. The SASIS model has
been an effective mechanism in performing numerous simulations within a flow range between
minimum and maximum, aiming to determine the relationship between flow rate and water application
efficiency, percolation and runoff rates and hence optimize the performance of furrow irrigation
systems with continuous flow.
Key words: Furrow irrigation, flow rate, performance.
INTRODUCTION
Although, surface irrigation is the most widely used it is
considered as low water application efficiency,
particularly in furrow irrigation systems, in which the
furrow is responsible for lower efficiency levels. Surface
3116
Afr. J. Agric. Res.
irrigation is an irrigation method where water drains by
gravity, using the agricultural soil surface as part of the
water distribution system. The flow rate decreases as the
water advances towards the irrigated portion due to
infiltration. For the infiltrated water to be distributed as
uniformly as possible along the area, irrigation should be
designed and managed so that there is a balance
between advance and water infiltration processes (López,
2006). Furrow irrigation is one of the most widely used
irrigation system due to its low cost in materials and
energy. The low efficiency of surface irrigation systems is
due to the large part of the absence of careful designing
and improper irrigation practices. According to Rezende
et al. (1988), reduced levels of performance in furrow
irrigation systems can be attributed to incorrect
dimensioning. The use of assessment tests would be
recommended, despite the high cost and time required
for the performance of field works and analysis of results.
Furthermore, it is virtually impossible to assess the
combined results of numerous parameters involved in the
designing and operation of systems.
To improve the efficiency of water application and
distribution, some designs have used the maximum nonerosive flow rate, reducing the flow when the advance
front reaches the end of the furrow. The efficiency of
furrow irrigation systems can often be improved by
reducing the inflow rate after water has advanced to the
end of the field, a common practice is to cut back to 50%
of the inflow (Clemmens, 2007). Another alternative is the
use of intermittent flow to distribute water in the furrows.
Both methods, despite showing improvement in the
performance of furrow irrigation systems, have the
disadvantage of requiring more labor and more
investment in equipment. In practice, it is observed that
the use of constant flow is predominant in furrow
irrigation designs, which is probably due to the farmer’s
tradition of using only one flow in the water application
during irrigation and to ease operation in the distribution
of water in the furrows.
Rodríguez et al. (2004), comparing surge irrigation and
conventional furrow irrigation for covered black tobacco
cultivation in a Ferralsol soil, found that surge flow furrow
irrigation with variable time cycles increased the
application efficiency by more than six fold, and the water
volume was reduced by more than 80% compared to
continuous irrigation. The largest rises in distribution
uniformity and reductions in percolation losses were
obtained with a furrow length of 200 m and a discharge of
-1
1 L s , respectively.
Valipour (2012), researching the management
strategies to increase the efficiency of furrow irrigation
obtained from simulations using the software SIRMOD,
found that the cutback and surge irrigation methods were
able to increase irrigation efficiency in 11.66 and 28.37%,
respectively. According to farmers the choice of limiting
regime inflow can identify the best input flow to achieve
maximum irrigation efficiency. The furrow irrigation
system presents different variables with regard to
operating system and with respect to field data, which
influence its performance. The operating system
variables are flow rate and time of application of water,
while the field variables are slope, size, roughness of the
surface, the furrow geometry and characteristic of water
infiltration into the soil. Infiltration depends primarily on
physical, chemical and biological soil properties, affecting
the advance and recession processes, and it is important
to estimate the optimal flow rate derived in an irrigated
soil (Walker et al., 2006). For a good furrow irrigation
design, one should consider these variables and their
interactions (Wu and Liang, 1970; Reddy and Clyma,
1981).
Valipour and Montazar (2012b), studying the
optimization of all effective infiltration parameters in
furrow irrigation, worked to achieve full irrigation status.
They used Genetic Algorithm Programming and MS
Visual Basic (VB) programming. The best equation of
distribution of curve water in the soil was determined by
using the MS Visual Basic (VB) programming. While for
the Genetic Algorithm (GA) coding in MATLAB software
environment, all effective infiltration parameters in furrow
irrigation were obtained for the best equation of
distribution curve of water in the soil. The found results
showed that by using VB and GA programming, water
delivery and farm size could be optimized.
The SWDC and WinSRFR models were evaluated by
Valipour and Montazar (2012a) to optimize the
parameters of furrow irrigation. They found that because
of the differences between the two models it was not
possible to say which one is better. However, in SWDC
model, input discharge becomes optimized, other
infiltration parameters could be optimized in furrow
irrigation using WinSRFR model and combining it with
SWDC model.
The mathematical simulation of surface irrigation is a
complex process of hydraulics and surface flow. These
processes have been simulated by computer models with
a large degree of complexity and accuracy (Strelkoff and
Katopodes, 1977; Elliott et al., 1982; Walker and
Humpherys, 1983; Strelkoff and Souza, 1984; Rayej and
Wallender, 1985), and such models simulate the advance
and recession of water over the soil surface and the
volume of infiltrated and percolated water.
The different models that simulate surface irrigation
have been developed to simulate an isolated irrigation
event, assuming that there is no spatial variation in field
parameters (infiltration, roughness, slope and cross
section). In practice, the validity of this hypothesis has
been found, considering that simulations have been very
*Corresponding author. E-mail: [email protected], Tel: 55 84 3317 8322.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
Lima et al.
close to measurement made in field along the crop
season. The objective of this study was to develop a
freely accessible mathematical computer model to
simulate and optimize furrow irrigation with continuous
flow in both languages Portuguese and English. The
model predicts through simulations of the advance
phase, the performance of an irrigation event and selects
the optimal flow rate in furrow irrigation systems with
continuous flow, that is, one that maximizes the water
application efficiency, balancing runoff and percolation
losses.
MATERIALS AND METHODS
This study uses the kinematic wave model which represents a
simplified form for the Hydrodynamic model. It assumes that there
is no height variation of flux with distance, the force due to the
weight component in the direction of flow is in balance with friction
forces, that is,  y  x  0 , completely neglecting the
momentum equation, leaving the continuity equation (Equation 1)
undetermined in term  A  t . To solve this problem, assuming
that there is a unique relationship that describes flow as a function
of the flow area, then the momentum equation is replaced by the
Manning equation (Equation 2). Therefore, the equations that
constitute the kinematic wave model become continuity equation:
A
Q
Z


0
t
x

(1)
S0 
Q2 n2
Q2 n2

A2 R 4 / 3 1 A 2
3117
(5)
Thus, obtaining Q, whose derivative, together with the infiltration
equation is used in equation 1, to yield a continuity equation of one
unknown dependent parameter, A. It is assumed that the spatial
and temporal dependence of Z are defined (Walker and
Humpherys, 1983).
According to Equations 2 and 5, this type of model does not
apply to furrows when the slope is very small or tends to zero.
According to FAO (1989), the maximum and minimal recommended
furrow slopes are 0.5 and 0.05%, respectively. In fact, their
accuracy will decrease when S0 approaches zero. Strelkoff and
Katopodes. (1977), found that the higher the longitudinal slope,
model simulates the flow conditions. Walker and Skogerboe (1987),
do not recommend this type of model to simulate the recession
phase.
Using Equation 4, the Manning equation becomes:
Q   Am
(6)

where:
m
1 s0
n
(7)
2
2
(8)
Where  and m = empirical constants.
Manning Equation:
A
Depletion and recession phases
Q 2n2
So R 4 3
(2)
Where A = flow cross-sectional area (m2), t = time (s), x = distance
of water advance in the field (m),  = infiltration opportunity time (s),
Z = infiltrated volume per furrow length unit (m3 m-1), Q = discharge
rate (m3 s-1), n = Manning roughness coefficient (m-1/3 s), So = field
slope (m m-1), R = hydraulic radius (m), cross-sectional area divided
by the wetted perimeter.
The Manning equation was used in this analysis to generate unique
relationship between flow and hydraulic section. Elliott et al. (1982),
proposed an empirical relationship for the hydraulic section, given
by:
y   1 A 2
(3)
and
A2 R1,33  1 A 2
Generally, it is said that when the flow is suspended, the crosssection area at the head end of the furrow immediately drops to
zero, favoring the statement that the depletion and recession
phases could be neglected; this is a reasonable assumption for
slope furrows, since the volume of water stored into the furrow is
very small at the flow cutoff time making the duration of the
depletion and recession phases very short, and therefore has a
small effect on the water infiltration profile. The behavior of the
recession phase is similar to the advance phase, but in the opposite
direction. In this work, the depletion and recession phases were
neglected, considering that the irrigation ends when water flow is
stopped, that is, it was assumed that the recession time is equal to
the flow cutoff time (Bernardo et al., 2009).
Over the years, infiltration has received much theoretical attention.
Today, there are many equations that describe infiltration such as
Kostiakov, Kostiakov-Lewis, Horton, Philip and Green-Ampt. In this
study, the Kostiakov-Lewis equation was used (Equation 9), as one
of the most widely used empirical expressions for furrow irrigation
modeling.
Z  k a  f o
(4)
Where y = flow height in the furrow (m), 1, 2, 1 and 2 = empirical
parameters that depend on the furrow shape.
The hypothesis described ensures that the potential functions
adequately describe relationships between flow height, crosssectional area, width of the free-water surface, hydraulic radius etc.
Through the Manning equation and according to Equation 4:
(9)
where  = infiltration opportunity time (s), k = empirical coefficient of
Kostiakov-Lewis infiltration equation (m² s-a), a = empirical exponent
of the Kostiakov-Lewis infiltration equation, dimensionless, fo =
infiltration rate (m3 m-1 s-1).
To calculate the maximum non-erosive flow rate, the SASIS
software was based on the method recommended by Walker and
Skogerboe (1987). The authors studied the maximum non-erosive
3118
Afr. J. Agric. Res.
flow rate as a function of parameters obtained from the furrow
dimensions and proposed the following equation:
Qmáx
 Vmáx  2 n 2
 
 3600 S 0 1
1
  2  2



(10)
Where Qmax = maximum non-erosive flow (m3 min-1), Vmax =
maximum non-erosive speed (m min-1), estimated by Walker and
Skogerboe (1987) 8-10 m min-1 in erosive soils and 13-15 in less
erosive soils.
Optimal flow
In the determination of the relationship between flow rate and water
application efficiency, percolation and runoff losses, numerous
simulations were performed by the SASIS model. The simulations
occurred in a flow rate ranging between the minimum and maximum
allowable, initiating at the minimum flow rate and increasing in the
rate of 2% until it gets to the maximum allowable flow rate. The
minimum flow rate is the one that guarantee the water will get to the
end of the field. The optimal flow rate considered by the SASIS
model through the successive simulations is the one that provides
the best irrigation performance and balance between runoff and
percolation losses.
Procedure for assessing the surface irrigation system
Assessing a surface irrigation system will identify various
management practices that can be implemented to improve the
efficiency of the irrigation system. These practices can be a
reduction in the flow rate and its time of application, changes in the
field length or maybe a combination of various strategies is
required. The main goal of the SASIS software is to help search for
surface irrigation management strategies, resulting in satisfactory
efficiency levels.
The assessment procedure of furrow irrigation proposed by
Walker and Skogerboe (1987) used in this analysis initially involves
the trapezoidal rule to integrate the subsurface flow profile, thus
determining the total infiltrated volume:
Vz 
L
Z o  2 Z1  2 Z 2    2 Z n1   Z n 
2n
(11)
Where Vz = infiltrated volume (m³), L = furrow length (m), Zi =
accumulated infiltration for point I (m3 m-1), n = number of segments
in which the furrow is subdivided.
The infiltration accumulated in each furrow segment is given by:
Z i  k t r  t a i   f o t r  t a i 
irrigation system or its management, the most common are
efficiency and uniformity. The evaluation parameters are defined in
various ways. There is no single parameter that adequately defines
irrigation performance. Conceptually, achieving adequate irrigation
depends on the amount of water stored in the crop root zone,
percolation losses (below the root zone), runoff losses, uniformity of
the water applied and on the remaining deficit in the root zone. After
all, performance means knowing whether irrigation optimizes or not.
When a field has uniform slope, the soil receives uniform flow at
its upper end and a wetting front will slowly advance at a
decreasing rate until it reaches the end of the field. If not blocked,
runoff will occur up to the end of recession. Figure 1 shows the
water distribution along the furrow length, arising from the
assumptions given above. The differences along the area in the
infiltration opportunity time produce water depths that are not
uniformly distributed - with a characteristic inclination to the end of
the field.
The water that can be stored in the root zone can be found by the
expression Vrz =
zone depth calculated on the project. But as shown, some region of
the root zone may not receive enough water due to the spatial
variation in infiltration distribution. The water depth that would
supply the root zone is Zreq, and the water that percolates below this
zone is lost[1] to drainage or groundwater system. Calculating each
of these components requires numerical integration of water
infiltrated along the field length, and according to the aim of this
discussion, it is convenient to define the components as follows:
Vrz = Water volume per width unit (1 m), which is actually stored in
the root zone;
Vdi = Water volume per width unit (1 m), corresponding to the
portion of the root zone that is not irrigated;
Vdp = Water volume per width unit or furrow spacing that percolates
under the root zone;
Vro = Water volume per width unit or furrow spacing that flows out of
the irrigated area;
Zmin = Minimum infiltrated water depth that generally occurs at the
end of the furrow; and
Zlq = Average water depth infiltrated in the 25% of the least irrigated
area.
Distribution uniformity refers to the water distribution in the soil
profile. Merriam and Keller (1978) proposed that the distribution
uniformity is defined as the average water depth infiltrated in the
25% of the least irrigated area (Zlq) divided by the average water
depth infiltrated in the entire area ( Z ). This term can be
represented by the symbol DU. The same authors also suggest an
absolute uniform distribution DUa, which is the minimum water
depth (Zmin) divided by the average water depth of the entire area.
The water application efficiency was accessed (Ea). For the no
deficit irrigation condition, the following equations were used:
a

Zi  k tcutoff  ta i

a

 f o tcutoff  ta i
Ea 
(12a)
Where k, a and fo = as defined previously, tr = recession time (min),
(ta)i = advance time for the i-th station (min).
For the purpose of project the flow cutoff time, tcutoff, replaces tr in
Equation 12a, according to Equation 12b.

(12b)
Measures to evaluate performance
Among the factors considered in evaluating the performance of an
( L * Z req )  Vdi , where Zreq is the required root
PL 
[1]
Z req L
Q . t cutoff
100%
Vz  Z req L
Q . t ccutoff
(13)
100%
(14)
Generally, these flows return to the reservoir and can be reused in another
place or in the same area. Thus, they are lost in terms of the irrigated area in
question, but perhaps not for the regional condition or basin. The negative
connotations of loss should be maintained to the area being irrigated, although
this water can be recovered and reused. The quality of these flows is almost
always not good and the reuse time should not be computed (Walker, 2001).
Lima et al.
3119
Figure 1. Components of the infiltration water profile in surface irrigation.
RL 100  Ea  PL
(15)
Where: Ea = water application efficiency, PL = percolation loss, RL
= runoff loss.
The definition of water application efficiency Ea, was standardized
as:
Ea 
Vrz
100%
Q * t cutoff
(22)
For the Z
noreqdeficit
condition it was assumed on the project
xd irrigation
Vzi
ZZreq
xd  Vzi of 100
(16)
Ea 
that
the
efficiency
water requirement (Er), also called storage
The water requirement efficiency, Er, which is also called storage
(16)
req xd  Vzi 100
E
Q
.
t
(16)
Eaa 
 ZQ
100
efficiency,
was
100%,
with
no
deficit
in
the
root
zone.
In
the
case
of
x

V
cutoff
req. tcutoff
d
zi
efficiency, was defined as:
(16)
E
100 equations were used:
deficit
theVfollowing
a  irrigation,
ZQ . txcutoff
req. t d
zi
cutoff
(16) Vrz
Ea  Q
100
Q .xtdcutoff
Er 
*100%
Z req
 Vzi
(16)
Ea  VVza ZZ
100
Z req * L
req xd x
(23)
req d %
Q. Ztcutoff
(17)
PL
req xd100
PL VzaQza
100
(16)
(17) (17)
PL
100
%%
. tcutoff
tcutoff
V QQ. .tZ
If the furrow shows infiltrated water depth smaller than required, the
req xd
(17)
PL  Vza cutoff
100%
infiltrated volume should be evaluated separately for the areas of
Z
x
za . t req d
cutoff
excessive and deficient irrigation. After identifying the furrow
(17)
PL  VQ
100%

Z
x
za
req d
section,
Q. tEcutoff
(18) xd, from which the infiltrated water depth is less than
RL 
 100

PL
(17)
PL
100
%
a
(17)
required,
the infiltrated volume will be calculated for the
(18) (18)
RL
EEa PL
Q .
tcutoff
RL 100
100
PL
a
appropriately irrigated area Vza, by equation 11 and for the
inadequately
irrigated area, Vzi, as follows:
(18)
RL  100  Ea  PL
(18)
Z req x d  V zi
(18)
RL
 100
 EVa zi100
PL
(19)
ERL
r  Z100
Vzi(19)
 Vz (18)
 Vza
req x 
 PL
Z reqxd LEaV
(24)
E r  Z req
100
dL
zi
Z
req
(19)
Er 
100
The volume of runoff loss per unit width is given by:
Z req Zx d LVzi
(19)
(19)
E r  Z xreq  V 100
Z
x dd L V zizi
req
Zlq req
req


L
.
Z
Z

lq
(19)
E r  
100DU  
Vro  Q
tcutoff  Vz
100
or
(20)
DU
 L . Z lq 100

(25)
 ZZZ
V
lq 
req L

V


dp  100
100 or DU  rz
(20)
DU  


Z lq 
 Z lq
The runoff loss (RL) is determined by equation:
 Vrz LV.dp
100 (20)
100 or DU  
(20)
DU  


L
.
Z


Z
 Zlq 
lq
V

V
rz
dp






or DU   LL. Z
DU   Z lq 100
100
 Vro (20)


.VZlq
min
min
100
lq
or
(20)
DU
aVrzL. Z
lq 
100
DUa  Z

100%
Z
or
(21)
DU
100
DU
100
RL

dp





or
(20)
DU  VrzV LV. V

DU   ZZZmin100
100
Zdpdp
Qt


min




rz




cutoff 
or
DU a V
rz  Vdp  100 (21)
DU a  Z  100
 (21)
(26)


 Z 
 Vrz  Vdp 
 L . Z min 
Z 

or
(21)
DU a 
DU a   min 100
100

L
.
Z
ZZmin


V

V
min
Z
L
.
Z



rz
dp
min
min 100


or
(21)
DU

100
DU


or
(21)
DUaa  L . Z
DUaa   Z 100
100
VVrz Vmin
 ZZmin 
Vdp 
3120
Afr. J. Agric. Res.
Table 1. Field data used in the validation of the SASIS model.
Field data
PISG1
PISG2
PISG3
Parameter
Clay-sandy
loam
[1]
1.33
67
0.0030
90
0.020
0.291
2.847
0.03781
0.165
0.000186
0.090
Clay-sandy
loam
[1]
1.47
100
0.0016
115
0.020
0.185
2.766
0.02931
0.302
0.000186
0.060
Sandy
loam
[1]
1.54
70
0.0043
41.7
0.025
0.532
2.840
0.01024
0.326
0.000264
0.020
-1
Flow (L s )
Furrow length (m)
-1
Slope (m m )
Cutoff time (min)
-1/3
Manning Coefficient, n (m s)
Parameter of section, 1
Parameter of section, 2
k (m3 m -a m -1)
a
fo (m3 min-1 m-1)
Zreq (m)
[1]
Flow rate adopted by the farmer in the field;
models.
[2]
PISG4
Soil type
Clay-sandy
loam
[1]
1.13
115
0.0024
86
0.020
0.339
2.806
0.0054
0.412
0.000186
0.020
KWF
Silty-clay
loam
[2]
1.50
360
0.0104
450
0.013
0.730
2.980
0.0088
0.533
0.00017
0.090
AMALGACQ
GUFCQ
Silty-clay
Silty-sandy
[2]
1.80
403
0.0066
500
0.013
0.730
2.980
0.00182
0.234
0.00019
0.090
[2]
1.30
217
0.0173
300
0.013
0.730
2.980
0.00896
0.0
0.000022
0.050
Flow determined in the design, used by the authors in the demonstration of the SIRMOD and SIRTOM
Table 2. Water advance data measured in the field and used in the validation of the SASIS model.
PISG1
[2]
XA
TA
0
0
6.70
2
13.40
4
20.10
6
26.80
9
33.50
13
40.20
16
46.90
20
53.60
23
60.30
27
67.00
32
[1]
[1]
PISG2
XA
TA
0
0
9.09
1.05
18.18
2.35
27.27
3.60
36.36
5.00
45.45
6.50
54.54
8.50
63.64
9.65
72.73
11.55
81.82
13.60
90.91
15.65
100.00 17.95
XA = advance distance (m);
[2]
PISG3
XA
TA
0
0
7
1
14
2
21
3
28
5
35
7
42
10
49
13
56
16
63
19
70
24
PISG4
XA
TA
0
0
11.50
3
23.00
5
34.50
7
46.00
10
57.50
14
69.00
17
80.50
27
92.00
40
103.50
48
115.00
66
KWF
XA
0
40
80
100
120
140
160
200
220
240
275
300
320
350
360
TA
0
5
14
20
30
37
48
75
89
102
130
150
170
200
208
AMALGACQ
XA
TA
0
0
31
12
62
22
93
30
124
46
155
53
186
68
217
85
248
98
279
120
310
140
341
155
372
191
403
232
GUFCQ
XA
TA
0
0
31
4
62
8
93
12
124
16
155
20
186
24
217
28
TA = advance time (min).
The SASIS model simulates the infiltrated water and the runoff
conditions in the field. Regarding the water that infiltrates the field
surface, the software determines depth of infiltrated water in the
root zone and how much percolates below this zone. Considering
that this information is determined for each point simulated in the
field, the data can be used for the calculation of various efficiencies
and uniformities.
The field data used in the validation of the SASIS model
corresponded to four data sets (PISG1, PISG2, PISG3 and PISG4)
collected in this research, relating to field assessments of furrow
irrigation events in the irrigated region of São Gonçalo, Brazil, two
data sets (AMALGACQ, private property and GUFCQ, Utah State
University farm in Logan, USA) published by Azevedo (1992) used
in the demonstration of the SIRTOM model, and one data set
(KWF-Kimberly Wheel Furrow) published by Walker and Skogerboe
(1987). The data for advance time measured in field, which was
used to validate the SASIS model is shown in the Table 2. The
SASIS model validation used both the measured flow practiced by
the farmers and that suggested by the authors Walker (1989) and
Azevedo (1992) in the demonstration of the SIRMOD and SIRTOM
models.
RESULTS AND DISCUSSION
The curves generated by the model proposed for the
irrigation performance as a function of the flow rate are
Lima et al.
shown in Figure 2. It was observed that in all studied
cases, when the flow rate increases, the water
application efficiency decreases, showing that this
parameter is much more affected by runoff losses than by
percolation losses. For the maximum non-erosive flow
rate runoff losses are maximal and percolation losses
minimal, whereas for minimum flow, that is, the one that
ensures that the water will get to the end of the area, the
opposite occurs. In addition, runoff losses are much more
sensitive to flow variations in relation to percolation
losses, a fact observed by the slopes of the curves. For
the studied cases, there were dominance runoff losses
over percolation losses, which occurred in the largest
flow range. This leads to a greater effect of runoff losses
in the water application efficiency value, making the water
application efficiency curve to present almost the same
behavior as the curve of percolation losses.
Figure 2a to f show the curves representing the runoff
and percolation losses intersect for a given flow,
indicating a change in the higher or lower effect of these
losses on the water application efficiency from this value,
as described in Table 3. It appears that, when there is a
balance between runoff and percolation losses, that is,
when they are balanced to the point that there is no
predominance of one over the other. High levels of water
application efficiency in furrow irrigation are achieved,
according to field data PISG1 (Figure 2a), KWF (Figure
2b) and GUFCQ (Figure 2c). However, for field data
PISG2 (Figure 2d), PISG3 (Figure 2e) and PISG4 (Figure
2f), runoff and percolation losses were high,
consequently,
low
maximum
water
application
efficiencies were obtained, respectively, 50.57, 34.28 and
48.51%.
Based on the maximum water application efficiency
simulated for all studied field conditions, the optimal flow
rate was found, as described in Table 3. In general, for all
the field conditions the optimal flow rate corresponded to
a value close to the minimum flow rate that was better
-1
accepted by the SASIS model, starting from 0.6 L s .
This tendency can be explained by the direct relation
existent between the water application efficiency and
water losses. The selection of the highest balance among
the water application efficiency and water losses leads to
values different from the highest flow rate, when the
water losses are maximum.
For PISG1 (Figure 2a), the optimum flow rate was
predicted by the SASIS model to be 1.05 L s-1, which is
close to the minimum (0.9 L s-1). Values of runoff and
percolation losses presented minimal discrepancy
between them for the optimal flow, only 2.88%, and large
discrepancy for maximum flow rate of 47.27%. This
resulted in a predicted water application efficiency of
82.42% using the optimal flow rate and 43.7% using the
maximum flow rate. It appears that the runoff loss was
largely affected by the flow, which does not occur with
percolation loss, a fact that can be justified by the type of
soil for this data, which was clay-sandy loam,
3121
characterized by low infiltration rate (k = 0.03781 and fo =
0.000186). For the flow rate adopted by the farmer in the
field, the values for water application efficiency and for
runoff and percolation losses were, respectively, 77.74,
16.20 and 6.06%, showing that percolation and runoff
losses presented values well balanced, with difference of
10.14% between them. The results demonstrate that the
smaller the difference between these losses, the better
the performance of the irrigation system. For the optimal
flow rate, the difference between percolation and runoff
losses was 2.88%, resulting in a water application
efficiency of 82.42%. The flow rate adopted by the farmer
approached the optimal value predicted by the SASIS
model.
For example KWF (Figure 2b), appears that for optimal
flow rate, the values of runoff and percolation losses
showed variation of only 9.91% between them, thus
reaching high performance level in this irrigation system.
Certainly, the low water infiltration rate (k = 0.0088 and fo
= 0.00017) in this clay-sandy loam soil greatly contributes
in obtaining small values for these losses. For the flow
rate adopted by the farmer, the values predicted for water
application efficiency, for runoff and percolation losses
were, respectively, 75.14, 14.47 and 10.39%, indicating
values close to those predicted for the optimal flow rate,
showing that this flow rate was a good option.
For field data GUFCQ (Figure 2c), it appears that the
curve that refers to the runoff losses presents high slope,
showing a large variation of the performance with the flow
rate, since the curve that indicates the percolation losses
has small slope, demonstrating that under these field
conditions, it was slightly affected by the flow rate. The
curve related to the water application efficiency shows
the same slope as that of the runoff losses, but in the
opposite direction. In this example, the difference
between these losses for the optimal flow rate was only
1.78%, showing that the smaller these losses and the
smaller the difference between them, the greater the
water application efficiency in furrow irrigation.
Furthermore, when optimal flow rate was applied, both
runoff and percolation losses were low, resulting in high
water application efficiency and when maximum flow rate
was applied, the percolation losses remained low, while
the runoff losses were very high, reaching 53.04%. It
could be concluded that the low percolation losses, either
for maximum or optimal flow rate, and the high runoff
losses for maximum flow rate, can be explained by the
low water infiltration rate in this type of soil (k = 0.00896
and fo = 0.00022) and also by the presence of steep
slopes in this area (0.0173 m m-1). For the design flow
rate (1.30 L s-1), the values predicted for the water
application efficiency, runoff and percolation losses were
44.65, 34.12 and 21.23% respectively, indicating that the
flow rate selected by the SASIS model was absolutely
inadequate.
For PISG2 (Figure 2d), the curves representing the
water application efficiency and the percolation losses
3122
Afr. J. Agric. Res.
(a)
(b)
2
80
100
Ea = 23.163x - 110.01x + 174.16
82.42
80.94
90
2
R = 0.9768
RL = -24.319x2 + 116.11x - 86.607
70
2
R = 0.9798
60
51.78
50
43.71
40
10.23
30
11.86
20
PL = -2.302x + 9.4976
R2 = 0.9721
10
Performance of Irrigation (%)
Performance of Irrigation (%)
100
Ea = 113.31x-0.6665
2
R = 0.9969
54.55
60
RL = 69.889Ln(x) - 30.057
43.66
2
R = 0.9914
40
-3.9698
4.17
20
PL = 109.49x
2
R = 0.8732
0.96
0
0
1
1.2
1.4
1.6
1.8
2
Flow rate (L s-1)
application efficiency
2.2
1.5
2.4
1.7
1.9
2.1
2.3
2.5
2.7
2.9
Flow rate (L s-1)
runoff
deep percolation
applicattion efficiency
runoff
deep percolation
(d)
95.7
93.94
100
90
-0,8958
Ea = 75.164x
80
2
R = 0.9965
70
60
53.04
RL = 62.95Ln(x) + 19.769
50
2
44.57
R = 0.9891
40
30
0.31
2.14
20
-0,6237
PL = 3.382x
R2 = 0.9999
10
Performance of Irrigation (%)
(c)
Performance of Irrigation (%)
84.31
81.75
80
80
RL = -4.2857x2 + 46.385x - 57.098
70
R = 0.9847
70.33
2
60
50.57
44.68
50
40
Ea = 80.689x-1.0064
30
2
R = 0.9939
20
PL = 75.406x-0.8936
R2 = 0.9965
10.35
0.46
10
0
1.5 1.8 2.1 2.4 2.7
13.97
3 3.3 3.6 3.9 4.2 4.5 4.8 5.1 5.4 5.7
6
-1
Flow rate (L s )
0
0.7
0.9
1.1
1.3
1.5
1.7
application efficiency
1.9
runoff
deep percolation
-1
Flow rate (L s )
application efficiency
runoff
deep percolation
(e)
(f)
70
64.59
63.24
70
RO = 64.261Ln(x) + 33.315
R2 = 0.9803
60
50
60.34
-0.9208
DP = 42.39x
R2 = 0.9882
34.09
33.35
40
26.96
30
20
1.32
10
Ea = 21.28x
3.41
R = 0.983
0
0.6
0.7
12.7
-1.0313
2
0.8
0.9
1
1.1
1.2
Flow rate (L s-1)
application efficiency
runoff
1.3
1.4
1.5
1.6
deep percolation rate
Performance of Irrigation (%)
Performance of Irrigation (%)
80
60
50
RL = -36.009x 2 + 142.05x - 83.24
R2 = 0.9919
48.51
58.53
47.06
47.07
40
-0.7267
PL = 38.19x
R2 = 0.9423
40.13
30
22.75
32.00
20
10
0
Ea = 36.667x
-1.0119
R2 = 0.9933
18.72
5.86
2.95
0.7 0.8 0.9
1
1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9
Flow rate (L s-1 )
application efficiency
runoff
2
deep percolation
Figure 2. Irrigation performance as a function of flow rate and optimal flow rate: (a) PISG1 data and 1.05 L s -1; (b) KWF data
and 1.62 L s-1; (c) GUFCQ data and 0.79 L s -1; (d) PISG2 data and 1.66 L s -1; (e) PISG3 data and 0.64 L s -1; (f) PISG4 data
and 0.77 L s-1. Ea – water application efficiency, RL - runoff loss and PL - percolation values.
Lima et al.
3123
Table 3. Simulated data for minimum, maximum and optimal flow rate and performance parameters based on field data.
Field
data
PISG1
KWF
GUFCQ
PISG2
PISG3
PISG4
[1]
Minimum
flow rate
(L s-1)
0.90
1.60
0.60
1.50
0.76
0.81
Maximum
flow rate
(L s-1)
2.33
3.01
1.79
5.92
1.56
2.02
Ea – water application efficiency;
[2]
Optimal
flow rate
(L s-1)
1.05
1.62
0.79
1.66
0.64
0.77
Maximum flow rate;
is very similar to the point of practically overlap,
with values very close to the same input flow. This
example shows a large difference between runoff
and percolation losses, since the difference
between them was 48.5% for optimum flow rate
and 54.63% for maximum flow rate, resulting in
low water application efficiency. The flow rate also
showed greater effect on runoff losses, with
difference of 69.88% between values predicted for
optimal and maximum flow rates, compared to
percolation losses, for which the difference was
-1
33.26%. For the design flow rate (1.47 L s ), the
values predicted for the water application
efficiency and for runoff and percolation losses
were 47.62, 0 and 53.16%, respectively. Thus,
showing values close to those predicted for the
optimal flow rate, demonstrating that the farmer
made the right choice for these field conditions.
For field data PISG3 (Figure 2e), the value
predicted by the SASIS model for the optimal flow
rate was 0.64 L s-1, close to the minimum flow rate
and for the maximum flow rate, it was 1.56 L s-1,
whose runoff losses were 0.78 and 60.45%,
respectively, while percolation losses were 64.94
and 26.88%, resulting in water application
efficiencies of 34.28 and 12.66%. That is also a
[3]
Flow rate
practiced by the
farmer (L s-1)
Minimum
[1]
Ea (%)
Optimal
[1]
Ea (%)
1.33
1.50
1.30
1.47
1.54
1.13
43.71
54.55
44.57
13.97
12.66
18.72
82.42
81.75
93.94
50.37
34.28
48.51
Runoff (%)
[2]
Percolation (%)
MFR
OFR
[3]
MFR
OFR
51.78
43.66
53.04
70.33
60.45
53.53
10.23
4.17
2.14
0.46
0.78
2.95
4.51
14.08
2.38
15.70
26.88
22.75
7.35
1.79
3.92
48.96
64.94
48.54
Optimal flow rate.
critical field condition for the irrigation system
management, whose percolation and runoff
losses show large difference for both optimal flow
rates as for the maximum flow rate. The difference
between them for the optimal flow rate is 64.16%,
and for the maximum flow rate is 37.57%. It was
observed in this example that the curve for the
percolation losses has basically the same
behavior as the water application efficiency curve
that is, similar slopes in certain flow rates,
however, always with different values. For the
-1
design flow rate (1.54 L s ), the values predicted
for the water application efficiency, runoff and
percolation losses were 12.7, 60.3 and 27%,
respectively, showing quite different predicted
losses, demonstrating that the flow choice will
cause large runoff losses, which indicates the
need for flow management to reduce difference
between these losses.
In the example of PSG4 (Figure 2f), the optimal
flow rate predicted by the SASIS model, the
difference between runoff and percolation losses
was 45.59%, and for the maximum flow rate the
difference was 35.78%. However, the results
demonstrated that the flow rate has a much
greater potential to affect the irrigation
performance by runoff losses than the infiltration
rate by percolation losses, considering that for the
same water infiltration conditions the water
application efficiency was 48.51% for the optimal
flow rate, while for the maximum flow rate, it was
18.72%. Figure 2f shows that the curves that
represent the water application efficiency and the
percolation losses presented the same tendency,
that is, the same slope with values quite close,
while the curve related to the runoff losses shows
high slope and values distinct from runoff losses
predicted for optimal and maximum flow rates.
When the flow rate practiced by the farmer (1.13 L
s-1) was used as input data to calculate water
application efficiency and runoff and percolation
losses, they were 38.25, 30.40 and 31.35%,
respectively. Both runoff and percolation losses
show very high values, which makes the water
application efficiency low, even below the value
for the optimal flow rate. Thus, in this example,
the choice of the flow rate was not adequate. The
fact that the values for water application
efficiency, runoff and percolation losses were very
close to each other is because the flow rate
selected by the SASIS model is close to the
intersection points of these curves.
3124
Afr. J. Agric. Res.
The results of this research show the need for
optimization in furrow irrigation systems with continuous
flow and also identify that in some field conditions, one
can achieve high performance levels. According to the
studied examples, it was found that the best furrow
irrigation performances with continuous flow are achieved
for flow rates near the minimum allowable. When runoff
and percolation losses are minimal, with little difference
between them, that is, with no predominance of one over
the other (total maximum losses around 20%), the best
water application efficiency is achieved. In some field
conditions, such losses cannot be controlled, that is water
application efficiency cannot be optimized, and another
flow rate which results in improved irrigation performance
must be selected. The analyses of the obtained results
show that high water application efficiencies were
achieved for the low infiltration soils: PISG1, KWF and
GUFCQ. While lower performances were achieved for
high infiltration soils: PISG2, PISG3 and PISG4.
In field conditions in which runoff and percolation
losses were controlled, soils have low infiltration rates; for
these infiltration conditions, it was possible to obtain
greater control of these losses for a furrow length range
from 67 to 360 m and slope from 0.030 to 0.0173 m m -1,
showing that the optimization of the furrow irrigation
system can be obtained in a wide range of length and
steepness. In field conditions in which this control is not
achieved, soils have high infiltration rates and the length
and slope ranges were 70 to 115 m and 0.0016 to 0.0043
m m-1 respectively, showing once again that length and
slope do not interfere in optimization.
Valipour (2012a), used the SIRMOD software to
compare the hydrodynamic models, zero inertia and
kinematic wave in surface irrigation. It was found that the
hydrodynamic models and zero inertia were very
accurate in the simulation process. However, when the
gradient field was increased up to 0.01, the zero inertia
and hydrodynamic models showed no difference, but for
values greater than 0.01, due to the water velocity
increasing, the zero inertia model failed. According to the
author, for the Manning roughness coefficient up to 0.15
the error increases, after this value the error remains
constant, and n = 0.15 is determined as critical flow. For
the author, the accuracy of the kinematic wave model is
reduced for heavy clay soils, high flow rates, elevated
Manning roughness coefficient and basin irrigation.
Runoff losses affect the performance of furrow irrigation
systems with continuous flow, since the low performance
levels of furrow irrigation systems used in this study
occurred due to the use of flow rates near maximum
allowable values, and it is believed that this fact occurs in
most areas where furrow irrigation with continuous flow is
practiced, explaining the low performance levels recorded
in literature.
According to Eldeiry et al. (2004), the high efficiency in
furrows with length from 25 to 50 m can be achieved with
small discharge. The authors affirm that furrows with a
length of 100 m had an efficiency of 80% with discharge
ranging from 0.05 to 0.10 m 3 min-1. For small furrows, the
efficiency is high for low flow rates with minor variations,
while for long furrows, greater efficiency is obtained with
less dependence on the flow rate, being the most widely
used.
Thus, this study demonstrated well the importance of
the SASIS model in forecasting the performance of
furrow irrigation systems with continuous flow, selecting
input flow in open furrow at the end of the area, allowing
better water application efficiency and avoiding waste of
water during irrigation.
Conclusions
The results showed that the flow rate plays a decisive
role on the performance parameters of irrigation systems,
with the best performances on flow rates. The analyses
of the obtained results show that high water application
efficiencies were achieved for the low infiltration soils,
while lower performances were achieved for high
infiltration soils. In soils with high infiltration rates, the
immense difficulty in optimization is to minimize
percolation losses, but in soils with low infiltration rates,
both percolation and runoff losses can be easily
minimized.
The SASIS model has effective mechanisms in
performing numerous simulations within a flow rate range
between minimum and maximum, aiming to determine
the relationship between flow rate and water application
efficiency, percolation and runoff losses and hence
optimize the performance of furrow irrigation systems
with continuous flow.
Conflict of Interest
The authors have not declared any conflict of interest.
REFERENCES
Azevedo CAV (1992). Real-time solution of the inverse furrow advance
problem. PhD diss. Logan, Utah: Utah State University, Agricultural
and Irrigation Engineering Department P. 526.
Bernardo S, Soares AA, Mantovani EC (2009). Manual de irrigação. 8°
Edition (2). Viçosa, Brazil: UFV.
Clemmens AJ (2007). Simple approach to surface irrigation design:
Theory EJ Land Water 1:1-19.
Eldeiry A, Garcia L, El-Zaher ASA, El-Sherbini Kiwan M (2004). Furrow
irrigation system design for clay soils in Arid regions. In Hydrology
Days. Colorado State University pp. 42-54.
Elliott RL, Walker WR, Skogerboe GV (1982). Zero inertia modeling of
furrow irrigation advance. J. Irrig. Drain. Div. ASCE. 108(IR3):179195.
FAO (1989). Irrigation Water Management Training Manual No 5. Food
and Agriculture Organization: (Ed.) Brouwer, C.; Prins, K.; Kay, M.;
Heibloem, M. Training manual no. 4.
López
RAA
(2006).
Métodos
de
Riego.
Available
at:
www.buenastareas.com/ensayos. Acessed 9 July 2013.
Merriam JL, Keller J (1978). Farm Irrigation System Evaluation: A Guide
for Management. Logan, USA: Utah State University.
Lima et al.
Rayej M, Wallender WW (1985). Furrow irrigation simulation time
reduction. J. Irrig. Drain. Div. ASCE. 111(2):134-146.
http://dx.doi.org/10.1061/(ASCE)0733-9437(1985)111:2(134)
Reddy JM, Clyma W. (1981). Optimal design of furrow irrigation system.
Trans. ASAE 24(3):617-623. http://dx.doi.org/10.13031/2013.34310
Rezende FC, Scalco MS, Scaloppi EJ, Scardua R (1988). Alternativas
de manejo e dimensionamento em irrigação por sulcos. In Proc. XVII
Congresso Brasileiro de Engenharia Agrícola, 102-114. Sorocaba,
CNEA/MA.
Rodríguez JA, Díaz A, Reyes JA, Pujols R (2004). Comparison between
surge irrigation and conventional furrow irrigation for covered black
tobacco cultivation in Ferralsol soil. Span. J. Agric. Res. 2(3):445458. http://dx.doi.org/10.5424/sjar/2004023-99
Strelkoff T, Katopodes ND (1977). End depth under zero-inertia
conditions. J. Hydraulics Div. ASCE. 103(HY1):699-611.
Strelkoff T, Souza F (1984). Modeling effect of depth on furrow
infiltration. J. Irrig. Drain. Engineer. ASCE. 110(4):375-387.
http://dx.doi.org/10.1061/(ASCE)0733-9437(1984)110:4(375)
Valipour M (2012). Comparison of Surface Irrigation Simulation Models:
Full Hydrodynamic, Zero Inertia, Kinematic Wave. J. Agric. Sci.
4(12):68-74.
Valipour M, Montazar AA (2012a). An Evaluation of SWDC and
WinSRFR Models to Optimize of Infiltration Parameters in Furrow
Irrigation. Am. J. Sci. Res. 128-142.
3125
Valipour M, Montazar AA (2012b). Optimize of all Effective Infiltration
Parameters in Furrow Irrigation Using Visual Basic and Genetic
Algorithm Programming. Austr. J. Basic Appl. Sci. 6(6):132-137.
Walker R, Prestwich C, Spofford T (2006). Development of revised
USDANRCS intake families for surface irrigation. Agric. Water
Manage. 85(1-2):157-164.
http://dx.doi.org/10.1016/j.agwat.2006.04.002
Walker WR (1989). SIRMOD, Surface irrigation simulation software.
Logan, USA: Utah State University.
Walker WR, Humpherys AS (1983). Kinematic-wave furrow irrigation
model.
J.
Irrig.
Drain.
Div.
ASCE
109(IR4):377-392.
http://dx.doi.org/10.1061/(ASCE)0733-9437(1983)109:4(377)
Walker WR, Skogerboe GV (1987). Surface Irrigation: Theory and
Practice. Logan, USA: Utah State University.
Wu IP, Liang T (1970). Optimal design of furrow length of surface
irrigation. J. Irrig. Drain. ASCE 96(3):319-332.
Vol. 9(42), pp. 3126-3132, 16 October, 2014
DOI: 10.5897/AJAR12.2156
Article Number: 201D26048065
ISSN 1991-637X
Copyright©2014
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Full Length Research Paper
Influence of physical protectors with different filters on
the initial development of Peltophorum dubium
(Spreng.) Taub seedlings
Jeferson Klein1*, João Domingos Rodrigues2, Vandeir Francisco Guimarães3,
Leandro Rampim3, Débora Kestring3, Daniel Schwantes3, Rubens Fey4, Valdemir Aleixo1,
Alfredo Richart1, Michelli Carline Ferronato1 and Augustinho Borsoi3
1
Pontifical Catholic University, PUC, City of Toledo, Parana state, Brazil.
São Paulo State University, UNESP, Biological Sciences, City of Botucatu, São Paulo State, Brazil.
3
State University of West Parana, Unioeste, CCA/PPGA, Pernambuco Street No. 1777, P.O. Box 9, Zip Code 85960000, city of Marechal Candido Rondon, Parana State, Brazil.
4
Federal University of South Border - UFFS, city of Laranjeiras do Sul, Parana State, Brazil.
2
Received 8 November, 2012; Accepted 9 September, 2014
The use of physical protectors has been considered as an efficient technique for tillage farming of
different species, mainly native ones. Based on the importance of the species, Peltophorum dubium for
revegetation of degraded areas, this study evaluated the emergence, survival and initial development of
P. dubium seedlings under the influence of physical protectors with different filters. Thus, the following
treatments were adopted: absence of physical protector (APP), transparent physical protector (TPP),
transparent physical protector + blue cellophane (BPP) and transparent physical protector + red
cellophane (RPP). The evaluated characteristics were: emergence velocity index (EVI), seedling survival
and emergence percentage, plant height, leaf area and root collar diameter. All of these physical
protectors increased the mean values of EVI and survival. In conclusion, the emergence speed and
initial development of P. dubium (Spreng.) seedlings grown in the interior of physical protectors,
independent on the filters, presented positive results. The reduction on the light intensity interferes
positively in the initial growth of these plants.
Key words: Native species, reforestation, seedlings.
INTRODUCTION
Since the beginning of the millennium, the reduction of
forest resources and the increase of the consumption of
wood products were widely discussed because of the
increasing need of reforested areas, mainly because, few
existent native areas are restricted to preservation, parks
and reserves (Mattei et al., 2001). The production of
native seedlings species with quality and good
characteristics are important for the success of P. dubium
*Corresponding author. E-mail: [email protected].
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
Jeferson et al.
seedlings populations. This production must be
performed using appropriate techniques which may
provide an efficient and secure quality control (Souza et
al., 2005), ensuring a production in quality and quantity
(Neves et al., 2005) being that the seedlings are resistant
to the adverse conditions. Thus, the direct seedling is a
versatile technique in the reforestation process (Barnett
and Baker, 1991). According to Ferreira et al. (2007),
native seedlings species consist of the dispersion of
seeds of the forest species being the seeds sown directly
in the reforestation area. This technique have the
advantage of reducing the implantation cost for forest
stands, in order to eliminate the nursery stage and labor
costs, while increasing its importance in areas that lack
these resources or with difficult access (Soares and
Rodrigues, 2008).
Derr and Mann (1971), studies with direct seedlings
observed that only one technique is not enough for the
protection of seedlings against adverse conditions. In this
way, the use of physical protectors allowed with direct
seedling become relevant, once it can cause the
reduction seeds and seedlings predation. Besides, the
advantage of its interference in germination and
establishment of species created a favorable
microenvironment (Ferreira, 2002; Mattei and Rosenthal,
2002).
In this way, the P. dubium (Spreng.) Taub. popularly
known in Brazil as “canafistula” or “faveiro” is a native
species found in all domain of the semi-deciduous forest,
as well as in the Brazilian cerrado, being that these
species are also profuse in secondary formations
(Donadio and Demattê, 2000). These species are
classified as an opportunist pioneer, tolerant to high taxes
of light and temperature, and also considered as a great
potential for the use of mix crops destined to the
recomposition of degraded areas and areas of permanent
protection (Lorenzi, 2000).
Overall, these species is prejudiced, because the
production of seedlings have restrictions on the
germination and initial development, which are related to
tegumentary characteristics and seeds dormancy
overcome with the use of physical and chemical
scarifying (Salerno et al., 1996; Mattei, 1999; Perez et al.,
2001; Teles et al., 2000; Oliveira et al., 2003; Paulino et
al., 2004; Viecelli et al., 2011), decreasing the efficiency
of seedlings obtaining.
However, other studies which quantify the increment in
the initial development of native forest species in function
of the light quality provided in the sowing point at the
reforestation area. A pilot study conducted at a laboratory
show that species as P. dubium present potential of
susceptibility to light quality variation (Klein et al., 2012).
Nonetheless, the real behavior or P. dubium in
reforestation areas with physical protectors at field level
was never evaluated. This procedure is crucial, mainly
during the critical period for the establishment of one
seedling comprehend in the first 30 days (Meneghello and
Mattei, 2004). It is during this period of time, the
3127
protection of the seeds is essential, mainly because they
can be exposed to dryness, soil compaction caused by
strong rainfalls, vulnerability to be attack by birds and
ants and also by the breaking in the loading and
transportation of the seedlings. In this way, the use of
physical protectors in the sowing of forest species is
important; it is also favorable to the formation of an
adequate micro clime to the germination and survival of
the seedlings (Lahde, 1974).
Different materials with different sizes, shapes and
colors had been used as physical protectors by many
researches (Barnett and Baker, 1991; Serpa and Mattei,
1999; Mattei et al., 2001; Klein, 2005; Klein et al., 2005;
Ferreira et al., 2007). According to Carneiro (1995), those
protectors must be light, non-toxic, hygroscopic and must
recover all soil surface. It is observed in the literature that
some benefits of the use of physical protectors when
used in the sowing points, contributes to the higher
emergence and survival rates in the first months for
plants of Pinus elliotti (Mattei, 1998; Mattei et al., 2001),
Pinus taeda (D’Arco, 1999) and in plants of Enterolobium
contortisiliqum and P. dubium (Malavasi et al., 2010).
However, there are no reports of physical protectors
associated with colored polymers. With all these aspects
mentioned above, this study aim to evaluate the influence
of physical protectors of different filters in the initial
development of P. dubium (Spreng.) Taub. seedlings.
MATERIALS AND METHODS
The experiment was performed between April to December, 2007,
in open area of the Botanic department (48º 26’ W, 22º 52’ S),
Institute of Biosciences, State University “Julio de Mesquita Filho”
(UNESP), Botucatu – Sao Paulo State, Brazil. The fruits were
collected from matrix trees located in the city of Botucatu – SP.
After collection, the seeds were removed from the fruits and clean
with the help of a sieve, and after that, they were mixed and
selected, thereafter, disposing the damaged ones.
The determination of the physical characteristics of the seeds,
was determined with the percentage of germination, level of seed
moisture, drying them at 103ºC for 24 h, weight of one thousand
seeds and number of seeds per kilogram. The calculations were
performed according to the recommendation of the Rules for Seeds
Analysis (Brasil, 2009). The seeds of canafistula were submitted to
manual mechanical scarification, by the use of a sandpaper number
P 80 (Piroli et al., 2005), for the overcome of dormancy (Viecelli et
al., 2011). After that, the seeds were submerse in water to room
temperature for 8 h and seeds which showed numbness and signs
of the imbibition were selected for the sowing. These seeds, were
taken to the experimental area for the sowing in vases of 12 liters
containing a Rhodic Hapludox, characteristic from the region, was
corrected and fertilizer 60 days before the sowing.
The test of germination was performed with five subsamples of
20 seeds, in a acclimatized room, with light exposure from
fluorescent lamps (20 W), internally fixed, with temperature
between 20 to 30°C and photoperiod of 8 h of light. The
experimental design was completely randomized, with four
treatments and ten replications. The physical protector used, was
constituted from P.E.T. bottles (polyethylene terephthalate), with a
volume of 2,500 ml without bottom and with cover. Different length
waves were obtained engaging the P.E.T. bottles with two layers of
cellophane paper with different colors (transparent, blue and red).
3128
Afr. J. Agric. Res.
Table 1. Evaluation of the physical characteristics and initial viability of
Peltophorum dubium (Spreng.) Taub. seeds, performed after 8 months of
storage.
Weight of one thousand seeds (g)
34.93
Number of seeds per kg
28,629
Seed moisture content
7.23
Germination
90%
Table 2. Speed Index of emergence Peltophorum dubium (Spreng.) Taub, subjected to treatment with the presence or
absence of colored protectors at 8, 11, 16, 24 and 48 days after sowing (DAS).
Treatment
Absence of physical protector
Transparent physical protector
Blue physical protector
Red physical protector
CV (%)
F
LSD
8
C
0.0
5.0B
7.5AB
10.0A
Days after sowing (DAS)
11
16
24
C
C
C
20.0
55.0
65.0
B
A
37.5
80.0
80.0A
A
A
62.5
82.5
82.5A
A
B
60.0
72.5
75.0B
6.23
14.82*
4.81
48
C
65.0
80.0A
82.5A
75.0B
Means followed by same letter in column do not differ significantly by Tukey test at 5% probability. CV, Coefficient of variation;
LSD, least significant difference.
This way, the following treatments were obtained: T1: no physical
protector; T2: transparent physical protector; T3: transparent
physical protector + blue cellophane and T4: transparent physical
protector + red cellophane. Daily, the following evaluations was
performed: emergence speed index (ESI): The seedlings
emergence evaluations were performed daily for 48 days after
sowing, seedlings which present cotyledonary leaves were
completely expanded and emerged; percentage of survival and
seedlings emergence: This was initiated after the first emerged
plant and it was concluded in 48 days after sowing, when the
protectors were removed to avoid the effect of the environment
pollution; height of plants: This was determined measuring the
distance from the root collar to the terminal bud of the plant;
diameter of the root collar: Measure performed with a digital caliper
and expressed in millimeters; and leaf area: This was defined as
the surface of the blade leaf measure performed with the use of an
instrument called area meter, model LI-3100 (cm2).
The obtained results were tested with the suppositions of
normality and homoscedasticity for parametric tests, by ShapiroWilk and Levene. After that, the analysis of variance (ANOVA) was
performed and regression analysis was used also with the program
SIGMA PLOT, which described the interactions between the
evaluated characteristics. The treatments means were compared by
the Tukey test at 5% of probability. For the evaluations of
emergence percentage and survival the means were transformed in
arccosine of the square root of x/100.
RESULTS AND DISCUSSION
The canafistula seedlings presented a percentage of
moisture around 7%. In relation to the number of seeds
per kilogram and the weight of one thousand seeds
(Table 1), the obtained results reveals that the seeds of
canafistula presented a common behavior just like the
most pioneer species, as described by Wanli et al.
(2001). In general, the canafistula present itself as a
great option for reforestation projects, due to its great
viability even with low content of water, high quantity of
seeds per kilogram and the presence of a dormancy
mechanism.
The first emergence of canafistula seedlings in the
presence of the physical protector, independent of the
color, initiate at 8 days after sowing and the maximum of
seedlings emergence was observed 16 days after sowing
(Table 2). In the other hand, the first emergences occur in
the absence of the physical protector detected, only at 11
days after sowing, being the maximum of seedlings
emergence observed only at 20 days after sowing. This
increase in the emergence speed is probably due to the
function of high taxes of temperature and moisture inside
physical protectors (Table 3).
This results, confirm what was observed by Klein et al.
(2005), the temperature inside the physical protectors,
P.E.T. bottles in three different heights. The same
authors observed the increase of nearly 2°C in the
interior of the physical protectors in relation to the
environmental temperature, no matter how high the
protector height.
The use of protectors was also responsible for the
increase of the soil moisture and the increase of the the
germination and survival study with different forest
Jeferson et al.
3129
Table 3. Temperature and air relative moisture in the seeding points from
Peltophorum dubium (Spreng.) Taub. 48 days after sowing.
Treatment
Absence of physical protector
Transparent physical protector
Blue physical protector
Red physical protector
CV (%)
F
LSD
Temperature
A
21.00
A
22.20
A
21.70
A
22.50
5.31
ns
1.02
1.67
Air moisture
B
47.90
A
68.90
A
69.20
A
69.80
12.50
10.93*
11.37
Means followed by same letter in column do not differ significantly by Tukey test at 5%
probability. CV, Coefficient of variation; LSD, least significant difference.
species in the no tillage system (Santos-Junior et al.,
2004). The same type of physical protector was also
responsible for the increase in the internal temperature of
this environment independently of the year station
evaluated (Klein, 2005).
In relation to the emergence speed index (ESI) inside
each treatment, those without cover and also with colors
presented quadratic behavior (Table 2). In this way, the
ESI of the seedlings presented higher values of
emergence between 20 and 30 days after sowing. In the
other side, for the decomposition of the response in each
evaluation (Table 2) significant difference was verified
between the treatments in all perform evaluations, where
higher ESI was also detected in the seedlings which were
inside the physical protectors, independently of the color
of the filter.
The emergence percentage of canafistula seedlings
presented an increase in all treatments between 16 and
24 days after emergence. But, the largest significant
difference was verify between 11 and 16 days after
emergence, where all the treatments with physical
protector, independently of the filter, originate superior
values when compared to the one observed in the
absence of the physical protector, being equal at 115,
107 and 100% for the blue protector (BLUE), transparent
protector (TRANSP), and red protector (RED),
respectively. This way, the higher seedlings emergence
may have occur because of the increase of the
temperature and phytochrome influence, probably
because, the physical protectors may have allowed the
canafistula seeds a higher period of time in the optimal
temperature for germination, that would explain the
maximal germination percentage in a very short time
(Silva et al., 2004).
In a study with Cedrela fissilis, Mattei. (1995) reports
that the used physical protector, a plastic cup of 250 ml,
without bottom and with the largest circumference of the
circle to the face down, promoted an increase in the tax
of germination. Mattei and Rosenthal (2002), notice that
protectors as plastic cups and paper cups, without
bottom, contributed in the initial emergence and in the
establishment of the canafistula seedlings evaluated at
18 months after sowing. Conflicting results were
observed by Ferreira et al. (2007), in a study with Senna
multijuga, observed significant effect for the physical
protector, which correspond to transparent plastic pots of
500 ml without bottom, putted above the sowing points.
The mean values of canafistula seedlings survival
emerged 48 days after sowing, independently of the
treatment, were 85% superiors (Table 4), which means
that the survival of the emerged seedlings was influenced
by the physical protectors independently of the color of
the filter. The weakness of seedlings in function to the
soil compression resulted because of the less moisture
observed as the principal factor of the cause of seedlings
death.
Serpa and Mattei (1999), observed a larger percentage
of survival in plants of Pinus taeda, used for timber and
cellulose originated in the interior the physical protectors.
Besides, Santos-Junior et al. (2004) observed an
increment in the survival percentage of Tabebuia
serratifolia, used for landscape and reforestation, while
physical protector used for transparent plastic cups of
500 ml without bottom and buried 2 cm under the hole,
was in relation to the absence of physical protector. The
increase, promoted by the physical protector in the
survival of seedlings can be directly related to the
ecological group which the vegetal species is classified
(Perez et al., 2001). In this way, the protector initiates
great benefits to slow development species (Ferreira et
al., 2007).
The mean values obtained at 48 DAS of plant height,
observe that the blue and red physical protectors
provided significant growth of stem in comparison to the
other treatments (Table 5). The transparent physical
protector promoted the third higher increment of this
variable, without significant difference in the height
provided by the absence of the physical protector. The
use of the physical protectors associated with color filters
in the points of sowing can interfere in the quality of light
in seeds and seedlings, stimulating or causing inhibition
in different physiological processes. According to
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Afr. J. Agric. Res.
Table 4. Mean values of seedling survival Peltophorum
dubium (Spreng.) Taub., submitted to treatment with the
presence or absence of colorful protectors 48 days after
sowing.
Treatment
Absence of physical protector
Transparent physical protector
Blue physical protector
Red physical protector
CV (%)
F
LSD
Survival (%)
B
878
A
932
A
966
954A
732
706*
452
Means followed by same letter in column do not differ significantly
by Tukey test ta t 5% probability. CV, Coefficient of variation;
LSD, least significant difference.
Table 5. Mean values of height, leaf area and root collar diameter of seedlings were Peltophorum dubium
(Spreng.) Taub., submitted to treatment with the presence or absence of colorful protectors 48 days after
sowing.
Treatment
Absence of physical protector
Transparent physical protector
Blue physical protector
Red physical protector
CV (%)
F
LSD
Height (cm)
5.3B
5.6B
A
9.3
7.6A
9.9
1.17*
1.86
2
Leaf area (dm ) Root collar diameter (mm)
9.9B
1.4A
11.0B
1.5A
B
A
15.3
1.8
A
21.3
1.1A
21.29
18.29
3.91*
0.45ns
8.63
0.84
Means followed by same letter in column do not differ significantly by Tukey test ta t 5% probability. CV, Coefficient
of variation; LSD, least significant difference.
Almeida and Mundstock (2001), many signaling when
presented in the different plant tissues can be
responsible for acting in the vegetal growth in function of
quality and quantity of the received light. For Taiz and
Zeiger (2004), the increment in the stem stretching
reveals the response to light intensity reduction on the
seedlings, which indicate the involvement of the
phytochrome in this perception.
The same behavior was found in Cattleya loddigessi,
where Braga et al. (2007) verified that the shoot system
and the root system grown were promoted by the red
mesh. The influence of the blue, black and red mesh in
the plant height was also verified in Aralia sp., Monstera
deliciosa, Aspidistra elatior and Asparagus sp. (Shahak
et al., 2002). The no tillage system in the presence of the
red physical protector provided higher mean values for
the leaf blade area when compared to the other
treatments (Table 5). The plants which was grown in the
interior of the red physical protector presented mean
2
values for leaf area around 21.30 dm , and the other
treatments without physical protector, transparent
physical protector and blue physical protector presented
9.90, 11.00 and 15.30 dm2, respectively. Those values
suggested a positive correlation between the reduction in
the light quantity received, photosynthetically active
radiation (PAR) and the values of shoot system area. In
this context, Larcher (2004), reports about the heliophytic
plants during the evolution and how this species develop
efficient strategies in order to maximize the electron
transportation to the chloroplast, even in low radiation
intensities. However, presenting high percentages of leaf
area can be one important alternative in reforestation
projects (Coelho Filho et al., 2005), mainly in relation to
the initial development of seedlings.
In the present study, the mean value of the root collar
in canafistula seedlings was not influenced by the
different treatments after 48 DAS (Table 5). It is important
to affirm that those treatments did not prejudice in the
initial development of canafistula in the evaluated period.
When the obtained mean values with the use of red
physical protector was analyzed it was observed that they
were 25, 31 and 40% respectively, lower to the ones
obtained in the absence of physical protector, transparent
physical protector and blue physical protector,
Jeferson et al.
respectively.
Guariz et al. (2006), evaluated the diameter and height
growth of Posoqueira acutifólia seedlings in different
solar radiation levels and it was observed that as the
shading level was increased, the root collar diameter
decreased. The same result was obtained by Aguiar et al.
(2005), analyzing the influence of different levels of
shading in the initial development of Caesalpinia echinata
seedlings which presented higher mean values in the root
collar diameter in relation to the plants maintained in full
sunlight.
Conclusion
All types of physical protectors increased the mean
values of EVI and survival. In the conditions of this study,
emergence speed and the initial development of P.
dubium (Spreng.) seedlings grown in the interior of
physical protectors, independently on the filters,
presented positive result. The reductions on the light
intensity interfere positively in the initial growth of plants.
Conflict of Interest
The authors have not declared any conflict of interest.
REFERENCES
Aguiar FFA, Kanashiro S, Tavares AR, Pinto MM, Stancato GC, Aguiar
J, Nascimento TDR (2005). Germinação de sementes e formação de
mudas de Caesalpinia echinata lam. (pau-brasil): efeito de
sombreamento.
Rev.
Árvore
29(6):871-875.
http://dx.doi.org/10.1590/S0100-67622005000600005
Almeida ML, Mundstock CM (2001). O afilhamento da aveia afetado
pela qualidade de luz em plantas sob competição. Ciência Rural
31(3):393-400. http://dx.doi.org/10.1590/S0103-84782001000300005
Barnett JP, Baker JB (1991). Regeneration Methods. In: Duryea ML,
Dougherty PM (Ed) Forest regeneration manual. London: Kluwer
Academic Publishers 3:35-50.
Braga FT, Oliveira C, Pasqual M, Castro EM, Almeida GW, Dignart SL,
Costa LCB (2007). Crescimento de Cattleya loddigesii "alba x alba"
in vitro mantida sob telas coloridas. In: Congresso De Ecologia Do
Brasil, 2007, Caxambu-MG. Anais... Caxambu VIII CEB, pp. 1-2.
Brasil. Ministério da Agricultura Pecuária e Abastecimento (2009).
Regras para análise de sementes. Brasília: SNDA/DNDV/CLAV. P.
399.
Carneiro JGA (1995). Produção e controle de qualidade de mudas
florestais. Curitiba: Universidade Federal do Paraná/FUPEP, UENF
451p.
Coelho Filho MA, Angelocci LR, Vasconcelos MRB, Coelho EF (2005).
Estimativa da área foliar de plantas de lima ácida 'tahiti' usando
métodos não-destrutivos. Revista Brasileira de Fruticultura
27(1):163-167.http://dx.doi.org/10.1590/S0100-29452005000100043
D'Arco E (1999). Estabelecimento e desenvolvimento inicial de Pinus
taeda L. em semeadura direta, sob efeito do preparo localizado do
solo, protetor físico e cobertura das sementes. Pelotas, 1999. 60p.
Dissertação (Mestrado em Agronomia) - Curso de Agronomia,
Universidade Federal de Pelotas.
Derr HJ, Mann Jr. WF (1971). Direct seeding pines in the south.
Washington, DC: USDA. Forest service. (Agricultural Handbook
391:68.
Donadio NMM, Demattê MESP (2000). Morfologia de frutos, sementes,
3131
e plântulas de canafístula (Peltophorum dubium (Spreng.) Taub.) e
jacarandá-da-Bahia (Dalbergia nigra (Vell.) Fr. All. ex Benth.) Fabaceae. Revista Brasileira de Sementes 22(1):64-73.
Ferreira RA (2002). Estudo da semeadura direta visando à implantação
de matas ciliares. Tese (doutorado em agronomia). Universidade
Federal de Lavras/MG. 138p.
Ferreira RA, Davide AC, Bearzoti E, Motta MS (2007). Semeadura
direta com espécies arbóreas para recuperação de ecossistemas
florestais. Revista Cerne 13(3):271-279.
Guariz HR, Faria PAS, Pezzopane JEM, Reis EF (2006). Avaliação do
crescimento em diâmetro e altura de mudas de canela (Posoqueira
acutifolia Mart.) sob diferentes níveis de radiação solar. In: X
Encontro Latino Americano de iniciação científica e VI Encontro
Latino Americano de pós-graduação, 2006, São José dos Campos.
Anais... São José dos Campos: UVP, pp. 2828-2830.
Klein J (2005). Utilização de protetores físicos na semeadura direta de
timburi e canafístula na revegetação de matas ciliares.. Dissertação
(Mestrado em agronomia) - Universidade Estadual do Oeste do
Paraná, Marechal Cândido Rondon / PR. P. 95.
Klein J, Kestring D, Schwantes D, Repke RA, Mezzalira EJ, Piva AL,
Arrua MAM, Javorski CR, Rodrigues JD, Guimaraes VF (2012).
Influence of light quality on germination and initial growth of
canephori seedlings Peltophorum dubium (Sprenge) Taub. J. Food
Agric. Environ. 10:947-951.
Klein J, Malavasi UC, Malavasi MM, Aleixo V (2005). Variação da
temperatura do ar em protetores físicos utilizados na semeadura
direta. In: III Jornada Científica da Unioeste, 2005, Marechal Cândido
Rondon/PR. III Jornada Científica da Unioeste pp. 152-158.
Lahde E (1974). The effect of seed-spot shelters and cold stratification
on pine (Pinus sylvestris L.). Folia For. 196:1-16.
Larcher W (2004). Ecofisiologia vegetal. São Carlos: RiMa. P. 337.
Lorenzi H (2000). Árvores brasileiras. Nova Odessa: Plantarum. P. 352.
Malavasi UC, Klein J, Malavasi MM (2010). Efeito de um protetor físico
na semeadura direta de duas espécies florestais em área de domínio
ciliar. Rev. Árvore 34(5):781-787. http://dx.doi.org/10.1590/S010067622010000500003
Mattei VL (1995). Preparo de solo e uso de protetor físico, na
implantação de Cedrela fissilis V. e Pinus taeda L., por semeadura
direta. Rev. Bras. Agrociência 1(3):127-132.
Mattei VL (1998). Materiais de cobertura em semeadura de Pinus elliotti
Engelm e Pinus taeda L., diretamente no campo. Rev. Bras.
Agrociência (4(1):64-68.
Mattei VL (1999). Semeadura direta de Peltophorum dubium (Spreng.)
Taub. No enriquecimento de capoeiras. Rev. Árvore 2:85-96.
Mattei VL, Romano CA, Teixeira MCC (2001). Protetores físicos para
semeadura direta de Pinus elliottii Engelm. Ciência Rural 31(5):775780.
Mattei VL, Rosenthal MD (2002). Semeadura direta de canafistula
[Peltophorum dubium (Spreng.) Taub.] no enriquecimento de
capoeiras.
Rev.
Árvore
26(6):649-654.
http://dx.doi.org/10.1590/S0100-67622002000600001
Meneghello GE, Mattei VL (2004). Semeadura direta de timbaúva
(Enterolobium contortisiliquum), canafístula (Peltophorum dubium) e
cedro (Cedrela fissilis) em campos abandonados. Ciência Florestal
14(2):21-27.
Neves CSVJ, Medina CC, Azevedo MCB, Higa AR, Simon A (2005).
Efeitos de substratos e recipientes utilizados na produção das mudas
sobre a arquitetura do sistema radicular de árvores de acácia-negra.
Rev.
Árvore
29(6):897-905.
http://dx.doi.org/10.1590/S010067622005000600008
Oliveira LM, Davide AC, Carvalho MLM (2003). Avaliação de métodos
para quebra da dormência e para a desinfestação de sementes de
canafístula (Peltophorum dubium (Sprengel) Taubert). Rev. Árvore
27:597-603. http://dx.doi.org/10.1590/S0100-67622003000500001
Paulino AF, Medina C, Azevedo MCB, Silveira KRP, Trevisan AA,
Murata IM (2004). Escarificação de um Latossolo Vermelho na pós colheita de soqueira de cana-de-açúcar. R. Bras. Ci. Solo 28(5):911917. http://dx.doi.org/10.1590/S0100-06832004000500013
Perez SCJGA, Fanti SC, Casali CA (2001). Salt stress and salt
temperature interaction on the germination of Peltophorum dubium
seeds. J. Trop. For. Sci. 13(1):44-61.
Piroli EL, Custódi CC, Rocha MRV, Udenal JL (2005). Germinação de
3132
Afr. J. Agric. Res.
sementes de canafístula Peltophorum dubium (Spreng.) taub.
tratadas para superação da dormência. Colloquium Agrariae 1(1):1318.
Salerno AR, Schallenberger TCH, Stuker H (1996). Quebra da
dormência em sementes de Canafístula. Agropecuária Catarinense
9(1):9-11.
Santos-Júnior NA, Botelho AS, Davide AC (2004). Estudo da
germinação e sobrevivência de espécies arbóreas em sistema de
semeadura direta, visando à recomposição de mata ciliar. Rev.
Cerne 10(1):103-117.
Serpa MR, Mattei VL (1999). Avaliação de diferentes materiais de
cobertura e de um protetor físico, no estabelecimento de plantas de
Pinus taeda L., por semeadura direta no campo. Ciência Florestal
9(2):93-101.
Shahak Y, Ratner K, Ovadia R, Giller YE (2002). Growing Aralia and
Monstera under colored shade nets. Olam Poreah July Issure 13:6062.
Silva EC, Nogueira RJ, MC, Neto ADA, Brito JZ, Cabral EL (2004).
Aspectos ecofisiológicos de dez espécies em uma área de caatinga
no município de Cabaceiras, Paraíba, Brasil. Iheringia 59(2):201-205.
Soares PG, Rodrigues RR (2008). Semeadura direta de leguminosas
florestais: efeito da inoculação com rizóbio na emergência de mudas
e crescimento inicial no campo. Sci. For. 78:115-121.
Souza VC, Andrade LA, Bruno RLA, Cunha AO, Souza AP (2005).
Produção de mudas de ipê-amarelo (Tabebuia serratifolia (Vahl.)
Nich.) em diferentes substratos e tamanho de recipientes.
Agropecuária Técnica 26(2):98-108.
Taiz L, Zeiger E (2004). Fisiologia Vegetal. Sinauer Associates Inc.
Sunderland, USA. P. 764.
Teles MM, Alves AA, Oliveira JCG, Bezerra AME (2000). Métodos para
quebra da dormência em sementes de leucena (Leucaena
leucocephala (Lam.) de Wit. R. Bras. Zootec. 29:387-391.
http://dx.doi.org/10.1590/S1516-35982000000200010
Viecelli CA, Fiorese EJ, Rampim L, Guimaraes VF (2011). Quebra da
dormência de sementes de canafístula (Peltophorum dubium
(Spreng) Taubert) e íris (Iris germanica). Revista de Biologia e Saúde
da UNISEP 4(2):28-35.
Wanli Z, Leihong L, Perez SCJGA (2001). Pré-condicionamento e seus
efeitos em sementes de canafístula (Peltophorum dubium (Spreng.)
Taub.). Rev. Bras. Sementes 23:146-153.
Vol. 9(42), pp. 3133-3138, 16 October, 2014
DOI: 10.5897/AJAR2014.9069
Article Number: 5480DCD48067
ISSN 1991-637X
Copyright © 2014
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Review
Overview of soil subsurface flow in hydrological
perspectives
Yinghu Zhang, Jie Yang*, Haijin Zheng and Jialin Jing
Jiangxi Provincial Institute of Soil and Water Conservation, Jiangxi Provincial Key Laboratory for the Control of Soil
Erosion, Nanchang, China.
Received 14 August, 2014; Accepted 9 September, 2014
With respect to chemical and biological perspectives, runoff generation mainly includes surface flow,
subsurface flow and groundwater flow. Subsurface flow as a pivotal phenomenon has been studied for
many years. Research on subsurface flow is a long-term challenge due to its complicated water flow at
the soil profile scales. In general, subsurface flow has two typical patterns: preferential flow (vertical
flow) and matrix flow (horizontal flow). In this paper, overview on subsurface flow including definition,
characteristics, mathematical models, trends and needs have been implied. Understanding the
mechanisms of subsurface flow is a huge future challenge from hydrological perspectives because
soils are gradually regarded as the most complex component on earth. Finally, research gaps, trends
and needs are listed in detail.
Key words: Subsurface flow, models, soil, surface flow.
INTRODUCTION
Soil particles are particularly important parts of the typical
soil-plant-atmosphere continuum (Philip, 1966). At the
soil profile scales, water movement and solute transport
is a common phenomenon when they move through the
total soil layers even groundwater reservoir.
Soil subsurface flow influenced by rainfall events, soil
depth, slope gradient, vegetation, cultivation, irrigation,
tillage systems and soil physical and chemical properties
is a typical flow pattern compared with surface flow.
Some hydrologists confirmed the relation of loss of
nitrogen (N) and phosphorus (P) components to soil
subsurface flow with respect to contaminants pollution.
Meanwhile, they illustrated the vital effects of soil
preferential flow (vertical flow as one kinds of soil
subsurface flow in soil layers) on pollutants preferential
transport. And the effect of soil matrix flow (horizontal
flow as one kinds of soil subsurface flow in soil layers)
was also significant to understand the comprehensive
effects of soil subsurface flow. From the year 1990,
people began to pay more attentions to the studies of soil
subsurface flow (Figure 1).
To prompt learning more about mechanisms of soil
subsurface flow whether at the profile scales or in the
landscape or global scales, maybe it is critical to
understand structure, rate, characteristics, controlling
factors, mathematical models, and methodology of soil
subsurface flow. In general, mathematical model
simulation, indoor and field experiments, theoretical
*Corresponding author. E-mail: [email protected].
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons
Attribution License 4.0 International License
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Afr. J. Agric. Res.
Figure 1. Publications of subsurface flow from 1967 to 2013 (source from web of science).
Figure 2. Subsurface flow process including soil preferential flow and soil matrix flow in the hillslope.
analyses are carried out to imply the characteristics of
soil subsurface flow in detail. During field experiments
process, it is difficult to observe subsurface flow
processes.
DESCRIPTION OF SOIL SUBSURFACE FLOW
As for subsurface flow, a small portion or the total portion
of rainfall could move through the upper soil layers, and
get to the groundwater level finally. Generally speaking,
subsurface flow phenomenon is obvious in humid regions
because soil infiltration rate is high there. As a pivotal
component of runoff generation, studies upon soil
subsurface flow which belongs to hydrological processes
are promising increasingly in the watershed. It is of great
importance to simulate runoff generation process.
In the last few years, more attentions had been paid to
soil subsurface flow studies by means of indoor
simulating events, and also excellent results were
showed.
However, researchers still disputed of soil subsurface
flow way patterns and resident time at the soil profile
scales. Currently, few attentions have been paid to soil
subsurface flow simulations, especially those controlling
factors which exert significant influences on subsurface
flow processes. Subsurface flow described by the runoff
generation process is showed in Figure 2.
Zhang et al.
Characteristics of soil subsurface flow
Soil subsurface flow mainly occurs in the unsaturated
zone where soil permeability is different from
surroundings. External controlling factors, such as soil
properties, rainfall, initial soil moisture and so on, exert
significant influences on soil subsurface flow.
In general, the effluent of soil subsurface flow takes
accounts for only 10 to 50% of the total runoff generation
in the watershed, while some hydrologists also stated that
the effluent of soil subsurface flow was larger than that of
surface or overland flow in some areas.
Though surface flow, subsurface flow and base flow are
usually correlated in hydrological perspectives, they tend
to be regarded as individual systems due to the
complexity among them when hydrologists concentrated
on their relationships.
WHICH CONTROLLING FACTORS COULD AFFECT
SOIL SUBSURFACE FLOW?
Soil texture and structure
From pedological perspectives, rock fragments or gravels
are used to characterize the forms of soil texture. In
usual, three different kinds of soil textures, such as low
sandy soil, intermediate sandy soil and high sandy soil,
are described in terms of rock fragments content. Soil
texture has a vital influence on soil moisture content and
soil pores, especially soil macropores and preferential
flow pathways. Generally speaking, preferential flow and
matrix flow are two aspects of soil subsurface flow. For
example, soil macropore flow usually occurs in the place
where silty sand is abundant. Meanwhile, finger flow
usually occurs in the place where coarse sandy soil is
abundant. Soil structure usually has a high spatial
heterogeneity because of soil macropore connectivity and
tortuosity, soil pore diameter and density and so on.
Changes of soil structure will lead to a complicated
heterogeneity with regard to large soil profile scales.
Rainfall and irrigation events
Rainfall intensity exerts on significant influences on the
effluent of soil subsurface flow directly, especially surface
flow. In general, two processes are involved during the
total rainfall events. On the one hand, soil water and
solute tends to move through the deep soils even
groundwater levels. On the other hand, soil water and
solute usually move along with surface flow as rainfall
intensity is larger than infiltration rate. Most studies
confirm that preferential flow pathways could not
disappear with the increase of rainfall intensity.
Meanwhile, water movement by means of matrix flow is
promising increasingly, while it is contrary for preferential
3135
flow. The effects of irrigation are the same as rainfall
events.
Vegetation
Vegetation plays a critical role in runoff generation,
especially soil water flow components, because
vegetation tends to bring about lag effects. Meanwhile,
three kinds of flows including low flows, intermediate
flows and high flows are characterized on the basis of the
conditions of vegetation.
Cultivation and tillage systems
With regard to farmlands, human activities have
significant influences on soil porosity, soil structure, soil
physical and chemical properties (Oostwoud and Poesen,
1999; Follain et al., 2012). Cultivation patterns including
tillage and no tillage systems could exert effects on the
proportion of soil aggregates during the liquid and
gaseous phases in the total soil ecosystem. Cultivation
tends to enlarge the infiltration time of soil water by
means of altering soil structure, finally increasing the
effluent of soil subsurface flow.
Soil physical and chemical properties
For soil physical properties, it mainly contains
porosity, soil structure, soil moisture, soil gaseous,
energies, soil evaporation and so on. While for
chemical properties, soil colloid and ion exchange,
PH, soil buffer action, soil redox properties are listed.
soil
soil
soil
soil
Plant roots and microorganism activity
Plant roots are able to form well-connected macropores
or channels and also normally grow into rigid pores
broader than their own diameters. Channels created by
plant roots may contribute to water and solute transport,
especially macropore flow. Li and Ghodrati (1994)
recorded that the saturated hydraulic conductivity (Ksm) in
soil columns with root channels was six time higher than
in control columns without root channels. Devitt and
Smith (2002) realized that there were likely to be fewer
pores with a lower root density as it was generally
accepted that roots generated macropores.
Rock fragments in the soil
Rock fragments containing all materials whose horizontal
dimensions are smaller than a pedon are stones and soil
mineral particles 2 mm or larger in diameter. Rock
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Afr. J. Agric. Res.
fragments distributed in the topsoil (at the soil surface,
well embedded in the topsoil and completely incorporated
in the topsoil) and subsoil gradually become emphasis,
especially soil surface rock fragments. Katra et al. (2008)
confirmed the influence of rock fragments on water
movement and reported that water content was high as
the soil surface containing rock fragments. Zhou et al.
(2011) demonstrated that there was a complex
relationship between rock fragments and water
movement and solute transport in undisturbed soil
columns.
wet/dry seasonal recycling, soil moisture content
correspondingly increases and decreases (Weiler and
Naef, 2003). As soil moisture content increases, soil
aggregates expand at the soil profile scale under wet or
freezing conditions, resulting in a decrease in macropore
density and in the area of flow water. However,
macropore tortuosity will be implied fully (Zhou et al.,
2009, 2011). On the contrary, soil aggregates are
expected to shrink because of low soil moisture content
under wet or thawing conditions, forming a series of large
pores that can facilitate flow rate or effluent movement.
Hillslope gradient and slope length
MATHEMATICAL MODEL OF SUBSURFACE FLOW
Generally speaking, hillslope gradient may depend on the
effluent rate of surface flow and subsurface flow to some
extent. Compared with gentle slope, steep slope has the
higher effluent rate of surface flow. Especially, it is easy to
form surface flow under steep slope conditions. While for
subsurface flow, it is complicated during the forming
process, because people could not change the boundary
conditions in the subsoil. With respect to steep slope,
only few portions of water will infiltrate into soil layers
under high rainfall intensity conditions. Water tends to
move along with the slope to form surface flow.
Meanwhile, slope length also has significant influences
on runoff generation. Some covering mulch distributed in
the soil surface may prevent soil water and solute from
being eroded when the slope is long. That is, soil water
will be obstructed along with the long slope.
Consequently, the effluent and flow rate decrease
increasingly as water get to the outlet sections.
Hillslope geometry
Hillslope geometry has a significant influence on
subsurface flow. Especially, hillslope length exerts pivotal
effects on hillslope travel time in the subsurface flow.
Meanwhile, lots of studies have paid more attentions to
the stable effluent rate of subsurface flow in the hillslope
considering the hillslope geometry.
Surface coverage
Surface coverage is a pivotal index when considering
runoff generation. Especially for the forest ecosystems,
surface flow or subsurface flow will be postponed
because of the interception from forest canopy, branch
and litter layers compared with bare land. With respect to
farmland, crops may also have the same effects. The
nutrition and fertilizer in soils will not be eroded with high
surface coverage.
On the basis of field experiments and events, simulated
models are be used to characterize the runoff generation,
such as surface flow, subsurface flow and base flow. But,
lots of simulated models are not suitable for runoff
generation process because of the complicated
controlling factors. Therefore, more and more
comprehensive indexes must be adjusted so as to
improve the accuracy. With regard to those models,
Richards’ equation, Kinematic wave model, Storage
discharge model, Tsinghua hillslope runoff model,
Instantaneous unit hydrograph model, and HYDRUS
models are widely used.
Richards’ equation
Richards’ equation was described by Richards (1931)
who took advantage of soil water movement continuity
principle and Darcy laws on the basis of micro
perspectives. The equation is described as follows:
  K s K r h   c

Q
t

represents Hamiltonian, K s
Kr
permeability
coefficient,
(1)
represents saturated
represents
relative
permeability coefficient, h represents total head, 
Q
represents pressure head, t represents time,
represents source term of any flow.
In general, it is impossible to solve the Richards
equation. Researchers only obtain some results by
means of data materials. According to the characterizing
analysis of this equation, three kinds of models are
described, such as one-dimensional Richards’ equation,
two-dimensional
Richards’ equation
and
threedimensional Richards’ equation.
Kinematic wave model
Freezing/thawing, wet/dry seasonal recycling
With the changes inherent to freezing/thawing cycles and
Kinematic wave model was described by Beven (1982)
who considered that the flow line in impermeable layers
Zhang et al.
distributed in the saturated zones was parallel to the
bedrock, meanwhile, hydraulic gradient was equal to the
bedrock gradient. This model is listed as follows:
q  K s H x sin 


H x
 H x
c
  K s sin 
i

x
 t
q
(2)
represents discharge per unit width, H represents the
thickness of saturated zones in the impermeable
boundary, i represents water flow rate from the
unsaturated zones to saturated zones per unit, c
represents the water holding capacity.
x
Storage discharge model
Storage discharge model was described by Sloan and
Moore (1984) who assumed that there was an
impermeable boundary in the hillslope. On the basis of
water balance theories, subsurface flow was studied from
macro aspects. Meanwhile, researchers supposed that
the gradient was  , slope length was L , and soil depth
was D . Relative index is described as follows:
V2  V1
q  q1
 iL  2
t 2  t1
2
3137
Most researchers simulate the loss of N and P even
other contaminants by observing the changes of
concentrations during the rainfall events. However, they
neglect that the concentration of compounds change
anytime. They do not consider each phase during the
experiments.
Water exchange between preferential flow pathways
and soil matrix is a complicated process. How many is
the transfer extent between preferential flow pathways
and soil matrix? Such should be measured accurately.
CONCLUSIONS
Overviews on subsurface flow studies including
subsurface flow definition, mathematical models, and
characteristics have been implied. Studies upon
subsurface flow are confronted with many difficulties
which could not be solved accurately. Conspicuously,
people should pay more attentions to preferential flow
and matrix flow if they would like to know more about
subsurface flow. As it is well known from the researches,
subsurface flow mainly includes preferential flow and
matrix flow. Therefore, understanding soil subsurface flow
is a huge future challenge from hydrological perspectives
because soils are gradually treated as the most
complicated component on earth.
(3)
V
represents discharged water volume in saturated zones
per width, t represents time, q represents discharge per
unit width, i represents water flow rate from the
unsaturated zones to saturated zones per unit, 1 and 2
represent start and end time.
PERSPECTIVES
Soil subsurface flow as a typical phenomenon in
hydrological perspectives has been treated as a
considerable index when hydrologists carry out studies to
imply the relation of hydrogeology to subsurface flow. To
date, people have concentrated on studying subsurface
flow increasingly. However, the complicated processes
among surface flow, subsurface flow and base flow
interactions confuse people to some extent. And
researchers could not characterize the mechanisms of
subsurface flow accurately. Some research needs are
listed as follows:
Conventional models should be adjusted to characterize
subsurface flow process in detail.
Subsurface flow mainly includes preferential flow and
matrix flow. During the rainfall events, what is the
proportion of water movement by preferential flow? And
what is the proportion by matrix flow? It is difficult to
quantify and simulate.
Conflict of Interests
The author(s) have not declared any conflict of interests.
ACKNOWLEDGES
This research was supported by the Natural Science
Foundation of China (No. 41361060).
REFERENCES
Beven K (1982). On subsurface stormflow: prediction with simple
kinematic theory for saturated and unsaturated flows. Water Resour.
Res. 18(6):1627-1633. http://dx.doi.org/10.1029/WR018i006p01627
Devitt DA, Smith SD (2002). Root channel macropores enhance
downward movement of water in a Mojave Desert ecosystem. J.
Environ. 50:99-108.
Follain S, Ciampalini R, Crabit A, Coulouma G, Garnier F (2012). Effects
of redistribution processes on rock fragment variability within a
vineyard topsoil in Mediterranean France. Geomorphology 175176:45-53. http://dx.doi.org/10.1016/j.geomorph.2012.06.017
Katra T, Lavee H, Sarah P (2008). The effect of rock fragment size and
position on topsoil moisture on arid and semi-arid hillslopes. Catena
72(1):49-55. http://dx.doi.org/10.1016/j.catena.2007.04.001
Li YM, Ghodrati M (1994). Preferential transport of nitrate through soil
columns containing root channels. Soil Sci. Soc. Am. J. 58(3):653659. http://dx.doi.org/10.2136/sssaj1994.03615995005800030003x
Oostwoud WDJ, Poesen J (1999). The effect of soil moisture on the
vertical movement of rock fragments by tillage. Soil Till. Res.
49(4):301-312. http://dx.doi.org/10.1016/S0167-1987(98)00185-8
Philip JR (1966). Plant water relations: some physical aspects. Annu
3138
Afr. J. Agric. Res.
Rev. Plant Physiol. 14:245-268.
http://dx.doi.org/10.1146/annurev.pp.17.060166.001333
Richards LA (1931). Capillary conduction of liquids through porous
mediums. Physics 1(5):318-333. http://dx.doi.org/10.1063/1.1745010
Sloan PG, Moore ID (1984). Modeling subsurface stormflow on steeply
sloping forested watersheds. Water Resour. Res. 20(12):1815-1822.
http://dx.doi.org/10.1029/WR020i012p01815
Weiler M, Naef F (2003). An experimental tracer study of the role of
macropores in infiltration in grassland soils. Hydrol. Process 17:477493. http://dx.doi.org/10.1002/hyp.1136
Zhou BB, Shao MA, Shao HB (2009). Effects of rock fragments on
water movement and solute transport in a Loess Plateau soil.
Comptes Rendus Geosci. 341(6):462-472.
http://dx.doi.org/10.1016/j.crte.2009.03.009
Zhou BB, Shao MA, Wang QJ, Yang T (2011). Effects of Different Rock
Fragment Contents and Sizes on Solute Transport in Soil Columns.
Vadose Zone J. 10(1):386-393.
http://dx.doi.org/10.2136/vzj2009.0195
Vol. 9(42), pp. 3139-3145, 16 October, 2014
DOI: 10.5897/AJAR2014.9009
Article Number: E8F907148069
ISSN 1991-637X
Copyright © 2014
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Full Length Research Paper
Efficiency impact of the agricultural sector on
economic growth in Togo
Kamel HELALI*, Koffi TSAGLI and Maha KALAI
Department of Applied Quantitative Methods, Faculty of Economics and Management of Sfax. University of Sfax,
Airfield Road Km 4, BP 1088, Sfax. 3018, Tunisia.
Received 15 July, 2014; Accepted 16 September, 2014
The fundamental idea of this article is to study the efficiency of the Togolese agriculture and its
relationship with economic growth on the basis of economies of scale. We tackled efficiency, in its
input and output orientations, using parametric and non-parametric methods in which an annual
product analysis is carried out. In this context, agriculture is modelled by the usual Translog and CobbDouglas functions and efficiency estimating techniques, such as the stochastic frontier analysis (SFA)
and data envelopment analysis (DEA). Agricultural production was analyzed on a product basis
depending on the cultivated area and the number of workers in the agricultural field. The study sample
consists of 15 agricultural products for the period running from 1990 to 2009. The Togolese agriculture
is generally inefficient according to the DEA and SFA methods.
Key words: Togolese Agricultural Sector, efficiency, stochastic frontier, data envelopment analysis (DEA).
INTRODUCTION
The agricultural sector in the developing countries is the
focal point of the policy makers and international
organizations that laid the foundation in the fight against
poverty. Agriculture, beyond its economic dimension,
remains a prerequisite for survival for the poor countries
threatened by famine. The major global organizations
that struggle for the welfare of the whole humanity put
agriculture at the heart of their preoccupations. Financial
assistance that developed economies grant to third world
economies is based on the idea that agriculture is a key
sector that should be promoted to back any idea of
development.
The main question that we try to answer in this work is
simple: Can we say that agriculture in the developing
countries is efficient? This question leads to another
more important one: What impact does the agriculture
efficiency have on the economic growth of these
countries? Or, more specifically, what role does an
efficient or inefficient agriculture have on the GDP of a
developing country? More clearly, our work raises the
question of the agriculture impact on economic growth
regarding efficiency.
An African country, particularly Togo, is going to be the
subject of our investigation since this country has
(Danklou, 2006; Thierry, 2010). In addition, more than
70% of the active workforce is engaged in agriculture;
more than 60% of its arable land, while only 11% of these
lands are being value according to official figures
*Corresponding author. E-mail: [email protected], Tel: 00216 74 27 87 77. Fax: 0021674279139.
JEL classification: D24, G21.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
3140
Afr. J. Agric. Res.
nevertheless, no signs of food self-sufficiency were
observed in Togo. For this reason, we really wonder if the
use of agricultural resources in Togo is optimal.
The issue here is to know whether economic growth is
caused by agriculture or vice versa. In other words, is
causality between agriculture and economic growth
simple or double? Obviously, it is very logical and justified
to say that it is agriculture that causes economic growth
because its importance in economic growth has long
been noticed by eminent economists, such as Lewis
(1956). Economic growth, by the savings made,
promotes the funding of research necessary for technical
progress that sustains agriculture. Technical progress
may reduce or complicate the appropriation of natural
resources by humans over other species. However, some
authors emphasize that economic growth measured by
the GDP tends to destroy the natural capital. One of the
criticisms of the market economy is that the environment
is poorly reflected in the current economic models. When
technical progress ignores the environmental constraints,
growth and productivity can have negative effects on the
environment. In short, agriculture contributes to economic
growth which has both beneficial and devastating effects
on it.
LITERATURE REVIEW
Obviously, it is very logical and fair to say that it is
agriculture that generates economic growth because its
importance has long been noticed by eminent
economists. Johnston and Mellor (1961) identified two
important relationships that distinguish the agricultural
sector in economic growth. Yorgason (1974) believes that
in almost all countries, agriculture is actually an important
sector. There is a temporal growth in the agricultural
sector whenever we notice a process of growth or
development. Besides, Johnston and Mellor (1961)
indicated that the importance of the transformation and
the size of the labor requirements and the capital needed
to develop a modern industrial sector, represent a large
burden for agriculture. These authors identified five
categories of agricultural contributions to the economic
development. These categories are the agricultural
production for domestic consumption, the exports of
agricultural products and substantial gains on external
trade, the transfer of labor in the industrial sector, the flow
of money for the capital development and the high
incomes in agriculture as a market for industrial products.
However, Kuznets (1968) identified only three categories
of
contributions:
product
contributions,
market
contributions and factor contributions.
Although Kuznets and Johnston-Mellor’s classifications
are artificially important, they did not reach the heart of
the problem. Naturally, the contributions of agriculture to
economic growth can take several forms. However, we
may wonder under which conditions these transfers
actually will take place. These classifications indicate
what might have happened without giving any real idea of
why the process starts or what must be done to maintain
or strengthen it. There have been attempts by some
economists to produce a theoretical basis for the
development of the transfer process. In general, they
used the model approach. Although they originally
tackled the problem using the transfer of goods and
factors, some market transfers were taken into
consideration.
Given that the GDP is a perfect measure of economic
growth, the debate will be formulated rather around the
causality direction between agriculture and economic
growth. It should be underlined that these two processes
(agriculture and economic growth) are the essential part
of our work. Nobody shall hold such a debate without
resorting to the concept of efficiency since the concepts
of efficiency and profitability cannot conclude this debate
in a convincing way. Efficiency will be the centre of inertia
of the triangle formed by the agriculture, economy of
scale and growth. The concept of efficiency borrowed
from the Life Sciences is a fundamental concept which is
of a great importance today due to the extent of its field of
application. In the managerial, social, and industrial
sciences, the concept of efficiency is omnipresent.
Today, even the health sector closely examined
efficiency. Our study of agriculture is entirely based on
the concept of efficiency. For this reason, agriculture will
be modelled as a company which uses the capital and
labor factors to achieve production.
The study of efficiency refers to the use of resources
available in production. The theoretical framework of the
efficiency measurement was originally developed by
Farrell (1957) in measuring the efficiency of firms or
production units within a production process. Efficiency is
the best use of resources in production. The decision
units, which manage to produce a maximum output from
a given level of inputs, or equivalently, a given output
from a minimum level of inputs can be considered
efficient. The approach is particularly interesting because
it uses a concept of relative efficiency, which helps avoids
setting a standard characterizing of the efficient
situations. A producer will then be considered to be
relatively inefficient if another producer uses a lower or
equal amount of inputs to produce as many or more
outputs. The production function estimation, which is the
relationship between the inputs and outputs of the
production process in the considered sample, enables
then to define the "best practices" located on the
production frontier (Djokoto, 2012). This frontier
represents the technological limits of what an
organization can produce with a given level of inputs. The
inefficiency of a decision unit is then measured according
to the distance from the frontier.
In what follows, we will go back, in a second section, to
a literature review that shows the causal relationship
between economic growth and agriculture. The data and
Helali et al.
3141
Table 1. Descriptive analysis of the variables.
Products
Sorghum
Millet
Maize
Fonio
(paddy) Rice
Yam
Sweet potato
Taro
Bean and Niébé
Voandzou and beans
Groundnut in Hull
Manioc
Cotton Granulates
Cocoa raw Coffee
Broad beans
Total
Production
Mean
S-D
572.0
82.9
562.3
101.6
418.4
96.7
146.9
19.6
142.7
35.4
61.7
18.8
53.2
14.6
36.0
9.8
31.1
7.3
15.9
5.5
14.8
4.7
8.8
8.3
6.3
1.2
6.0
4.0
6.0
5.7
2082.1
286.0
Capital
Mean
512.2
143.0
58.7
56.2
51.8
51.5
39.0
36.0
35.9
33.9
29.2
10.1
8.4
4.5
1.7
1072.1
Labor
Mean
191.1
20.7
7.8
10.2
12.2
14.0
1.4
1.3
1.3
1.2
1.0
6.7
8.3
1.6
1.6
224.7
S-D
127.2
109.3
77.4
68.1
59.1
56.7
28.0
18.8
14.6
14.4
8.2
5.9
4.0
1.8
0.3
593.9
Mean
63.9
54.9
38.9
34.2
29.7
28.5
14.1
9.4
7.3
7.2
4.1
2.9
2.0
0.9
0.2
298.4
S-D, Standard deviation.
models will be presented in the third section. The
parametric and non- parametric estimates of the
efficiency scores will be the subject of the fourth section.
Finally, the main conclusions will be presented in the last
section.
METHODOLOGY
Data and variables
Efficiency seen from the angles of its input and output will be
measured in a technical and allocative way through non-parametric
and parametric methods. In fact, we focus on a third world
economy, Togo, about which the data are obtained mainly from the
Statistics Centre of Togo. Each variable is characterized by panel
data with 15 agricultural products over a period of 20 years, which
gives us 300 observations for each variable. The Translog and
Cobb-Douglass functions will serve as working tools whereas the
ordinary least squares methods help us estimate the coefficients of
the models. The efficiency estimate will be ensured using the SFA
and DEA methods. Finally, a special attention will be paid to the
interpretation of the results.
Agricultural data about Togo are very scarce today. The available
ones stopped to exist in 1999 or 2001 at the General Directorate of
Statistics and National Accounts of Togo (DGSCN). Although these
data were of great help for us, we had to complete them in the field
through surveys conducted over three months in some Togolese
leading agricultural towns, such as, Tsévié, Kpalime Togoville
Atakpamé, Sodoké, Dapaong and Sinkassé. In total, we could
collect the information required to fill out the gaps left by the
DGSCN to reach a 20-year period (1990 to 2009).
We selected a sample of 15 agricultural products to model the
total agricultural production. These outputs include: Millet, maize,
sorghum, fonio, rice, yam, sweet potato, taro, cassava, beans,
cowpeas, bambara groundnuts, inshell peanuts, cotton seeds,
cocoa beans and green coffee. The reader should have the right to
wonder about the choice of these products. The answer is simple:
They are the products selected by the DGSCN to collect national
statistics which are more representative of the agricultural
production in Togo since these products represent more than 80%
of the Togolese agricultural production. The purpose of this choice
is to make the Togolese authorities understand this work easily so
that they will eventually use it for their future agricultural policies.
The food products are at the forefront in terms of volume since they
represent more than 75% of the production of this sample. The
prevalence of food crops is due to the period chosen for the
research. In total, worked work with three variables, namely
production (Y), capital stock (K) and labor (L) over a period from
1990 to 2009.
Our major factor is the overall area of the cultivated area in Togo
which is estimated at 1072100 ha per year (Table 1), that is to s ay,
approximately 30% of the arable land in Togo. This area is
increasing at a rate of 2.72% per year. The labor factor is the
number of inhabitants in the agricultural sector in general. On
average, 593900 inhabitants work in the agricultural sector each
year whose number can vary more or less by 298400 inhabitants
per year (Thierry, 2010). The overall production is measured in
volume because we do not have the prices of the various products
over the studied period. The average production in Togo is of
2082100 tons with an increase of 1.71% per year.
Models
Using two production factors, namely, capital and labor, as well as
a trend component to measure evolution over time (t), we limit our
work to the use of the Cobb-Douglas and Translog functions (Eric
et al., 2004).
The Cobb-Douglas production function is then defined by:
LnYit   0   L Ln Lit   K Ln K it   t t   it
(1)
Similarly, the Translog production function takes the form:
3142
Afr. J. Agric. Res.
Table 2. Unit root test of (Levin et al., 2002).
Model 1
Variables
*
B
LY
LK
LL
LK2
LL2
LKL
Model 2
T
T
T
T
1.55(0.939)
5.70(1.000)
5.1(1.000)
5.55(1.000)
4.86(1.000)
5.65(1.000)
1.54(0.939)
5.70(1.000)
5.1(1.000)
5.55(1.000)
4.86(1.000)
5.65(1.000)
3.3(0.990)
-0.94(0.17)
-6.132(0.000)
4.12(0.939)
-1.71(0.156)
5.47(1.000)
2
Model 3
*
C
*
B
*
C
18.02(1.000)
2.03(0.97)
-3.45(0.000)
8.27(0.939)
2.58(0.990)
10.02(1.000)
TC*
*
B
T
10.35(1.000)
-12.55(0.000)
-48.61(0.000)
-2.60(0.004)
-30.71(0.000)
-13.026(0.000)
32.17(1.000)
-10.03(0.000)
-48.7(0.000)
-0.42(0.33)
-27.46(0.000)
-3.91(0.000)
2
LY = Ln(Y); LK = Ln(K); LL = Ln(L); LK2 = 0.5*(Ln(K)) ; LL2 = 0.5*(Ln(L)) ; LKL= Ln(K)*Ln(L). P-values are put between brackets.
Table 3. Co-integration tests of the various variables.
Tests
Pedroni (1999)
Mc Coskey and Kao (1998)
Statistics
Panel ν−statistic
Panel ρ−statistic
Panel PP−statistic
Panel ADF−statistic
Group ρ−statistic
Group PP −statistic
Group ADF −statistic
LM
1
1
1
2
2
 LL  Ln Lit    KK  Ln K it    tt tt 2
2
2
2
  LK Ln Lit Ln Kit  tL tLn Lit  tK tLn Kit   it
LnYit  0   L Ln Lit   K Ln Kit  t t 
(2)
To know if the Cobb-Douglas production function advantageously
substitutes the Translog specification, we used the likelihood ratio
test (LR) to see whether the general Translog functional form, as
specified, dominates the Cobb-Douglas functional form. Therefore,
we marked by M1 the Cobb-Douglas model and M2 the Translog
specification.
Integration analysis
As previously specified, the efficiency estimation is made through
two types of methods: the parametric (SFA) and the non-parametric
(DEA). However, before we get there, it would be interesting to
study the correlations between the study variables.
The used transformed variables are spread over a 20-year
period. We therefore need to justify the long-run relationship
between them. The tests proposed by Levin et al. (2002) show that
none of the variables is stationary in level (Table 2).
Since the variables are not stationary (I[1]), it is necessary to
check the existence of a co-integration relationship between the
long-term variables of the model (Table 3). Actually, the tests of
Pedroni (1999) and McCoskey and Kao (1998) show that the
variables are co-integrated and, therefore, the long-term equilibrium
relationship is justified.
EMPIRICAL ANALYSIS
Estimation models
Concerning the parametric analysis, the efficiency of the
Cobb-Douglas
-3.37 (0.00)
-56.38 (0.03)
-16.59 (0.00)
-2431.4 (0.00)
-56.02 (0.37)
-15.31 (0.00)
-19.52 (0.00)
20.5208 (0.00)
Translog
0.14 (0.00)
-41.79 (0.00)
-12.38 (0.06)
-2460.8 (0.00)
-54.29 (0.37)
-14.18 (0.37)
-16.64 (0.00)
138.4012 (0.00)
agricultural sector in Togo was estimated using four
models: A Cobb-Douglas model with and without
technical progress (M1) and (M3) and a Translog model
(M2) and (M4) with and without technical progress (Table
4).
In most models (M1, M3 and M4), the results show that
the capital and labor, the factors production as well as the
trend and the constant are all significant. Moreover, for
M2, which represents the Translog function with a nonneutral technical progress model, the results show that
the capital output factor is significant whereas the labor
and trend are insignificant. This means that agricultural
production in Togo depends on the area of the cultivated
land and the number of workers in the plantations.
Actually, this production induces an upward trend for
M1 of 2.4% per year. Hence, if the cultivated area
increases by 1%, production rises by 0.94%, and if the
number of workers goes up by 1%, production rises by
0.18%.
The minimum log production in Togo is 1.673, which
gives a real production of 5328 tons per year. However,
at the level of the M4 model, production induces an
upward trend of 3.3% per year. Thus, if the cultivated
area increases by 1%, production rises by 0.9%, and if
the number of workers increases by 1%, production goes
up by 0.27%, that is a minimum production of 5640 tons
per year.
The LR tests of maximum likelihood help choose the
model that best describes the Togolese agricultural
production. Indeed, comparing M1 and M3 models gives
Helali et al.
3143
Table 4. Estimates of the models through the SFA.
Variables
0
K
L
t
Cobb-Douglas
M3
M2
M4
1.673(0.000)***
1.631(0.000)***
1.862(0.000)***
1.730***(0.000)
0.943(0.000)***
0.905(0.000)***
0.968 (0.000)***
0.924***(0.000)
0.187(0.003)***
0.229(0.000)***
0.158(0.291)
0.266***(0.000)
0.024(0.002)***
0.021(0.001)***
0.021(0.228)
0.033***(0.001)
-
-
-0.034(0.01)***
-0.418***(0.01)
-
-
0.017(0.67)
-0.020*(0.09)
-
-
-0.000(0.59)
0.001*(0.07)
-
-
0.005*(0.70)
0.008(0.468)
0.005(0.000)***
-
-0.003(0.147)
-
0.005(0.002)***
-
0.005(0.240)
-
1.648(0.000)***
1.661(0.000)***
1.684***(0.000)
1.659***(0.000)
0.002(0.000)***
0.001(0.614)
0.003*(0.097)
0.002(0.178)
1.069
1.133
1.010
1.060
0.972
0.973
0.971
0.973
55.02
48.10
58.28
57.12
 KK
 LL
 tt
 LK
 tK
 tL


²

Log-Likelihood
Translog
M1
*, **, *** Significance at 10, 5 and 1%.
a value of the likelihood ratio equal to 13.84 above the
critical value of the chi-square table at 5% (7.11), which
makes us reject the null hypothesis. In other words, M1
model is preferred to M3, and the Cobb-Douglas model
for non-neutral technical progress is preferable in this
case.
Furthermore, the comparison of M2 and M4 models
gives a value of the likelihood ratio equal to 2.32 but
inferior to the critical value of the chi-square table at 5%
(7.11), which makes us accept the null hypothesis.
Consequently, the M4 model is preferred to M2. For the
Translog specification, we accept the model with a
neutral technical progress.
Finally, the comparison of the M1 and M4 models gives
a value of the likelihood ratio equal to 4.2 but inferior to
the critical value of the chi-square table at 5% (7.11).
Therefore, the null hypothesis where the M4 model is
preferred to M1 is accepted. In the end, the Translog
specification with a neutral technical progress is better
than the Cobb-Douglas one.
Estimation of technical efficiencies in the agricultural
sector of Togo
From the above estimates, we will discuss the estimation
of the efficiency scores of the Togolese agriculture.
Actually, the SFA method shows the following efficiency
scores related to the various specifications selected
above: 75.3, 75.6, 75.8 and 76.3% (Figure 1). These
scores show that the rate of the input use is generally
acceptable (Somayeh et al., 2012). More precisely, the
Togolese farmers can use at least 25% of the agricultural
resources to achieve the same level of production.
Rice, maize, bambara groundnut, peanut, coffee bean,
cocoa are products that have efficiency scores above the
found average scores (Figure 2). These five products are
all grown in highlands where agriculture is flourishing.
This area receives annually a large amount of rainfall and
farmers there are very laborious. Cocoa takes the top
spot with 97.6% while the yam is around 7%. Products
that have high efficiency scores are either for exportation
or food products mainly for consumption.
Using the SFA approach, we could see that the
Togolese agriculture has a minimum efficiency of 75%,
which means that is very efficient in using these
resources. Besides, we can observe a uniformity of
efficiencies along the 20-year study period. The
consistency of this efficiency shows that farmers are
steady in the management of the agricultural resources.
We noticed that the labor force shrinks through time
whereas efficiency remains stable. Furthermore,
3144
Afr. J. Agric. Res.
77,5%
77.5
100%
100%
90%
90%
80%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%0%
76,5%
76.5
76,0%
76.0
75,5%
75.5
75,0%
75.0
EFFCDNTP
EFFCDTP
EFFTLNTP
2009
2008
2007
2006
2005
2004
2003
2002
2001
2000
1999
1998
1997
1996
1995
1994
1993
1992
1991
1990
74,5%
74.5
EFFTLTP
EFFDEACRS
EFFDEACRS EFFDEAVRS
EFFDEAVRS
Figure 1. Yearly average efficiency with SFA. EFF: Efficiency, CD:
Cobb-Douglas, TL: Translog, NTP: Neutral Technical Progress,
TP: Technical Progress.
Cocoa
Cocoa
Coffee
Coffee
Bean
Bean
Groundnut
Groundnut
Maize
Maize
VOANDZOU
VOANDZOU
Rice
Rice
Sorghum
Sorghum
Millet
Millet
FONIO
FONIO
Potato
Potato
MANIOC
MANIOC
Cotton
Cotton
Taro
Taro
Yam
Yam
Percentage
100%
100%
90%
90%
80%
80%
70%
70%
60%
60%
50%
50%
40%
40%
30%
30%
20%
20%
10%
10%
0%
0%
EFFCDNPT
EFFCDNPT EFFCDTP
EFFCDTP EFFTLNTP
EFFTLNTP EFFTLTP
EFFTLTP
Figure 2. By product average efficiency with SFA
70%
70%
Percentage
65%
65%
60%
60%
55%
55%
50%
50%
45%
45%
1990
1990
1991
1991
1992
1992
1993
1993
1994
1994
1995
1995
1996
1996
1997
1997
1998
1998
1999
1999
2000
2000
2001
2001
2002
2002
2003
2003
2004
2004
2005
2005
2006
2006
2007
2007
2008
2008
2009
2009
40%
40%
35%
35%
EFFDEACRS
EFFDEACRS
Yam
Yam
FONIO
FONIO
Millet
Millet
MANIOC
MANIOC
Cotton
Cotton
Taro
Taro
Potato
Potato
Maize
Maize
Sorghum
Sorghum
VOANDZOU
VOANDZOU
Groundnut
Groundnut
Rice
Rice
Coffee
Coffee
Bean
Bean
Cocoa
Cocoa
Percentage
Percentage
77,0%
77.0
EFFDEAVRS
EFFDEAVRS
Figure 3. Yearly average efficiency with DEA. EFF, Efficiency;
DEA, DEA method; CRS, constant returns to scale; VRS, variable
returns to scale.
production remains constant or tends to decline slightly.
We can see from this that agriculture is being specialized
and modernized because the minimum number of
farmers who keep domestic production constant owe it to
an efficient work organization.
On the basis of the non-parametric DEA approach,
overall efficiency is estimated at 61.2% for constant
returns to scale and 45.8% for variable returns to scale
Figure 4. By product average efficiency with DEA.
(Figure 3). The highest efficiency is that of the cocoa,
with 83.6%. Beside cocoa, the other products that have
efficiency above 80% are coffee and beans (Figure 4).
We can wonder why coffee and cocoa are export
products; is it because they are the most favorable
cultivated products? We do not think so because cotton,
the Togolese main export product, is efficient only at 49%
whereas the bean, which is a food product for local
consumption, reached an efficiency score of more than
80%. The most frightening case is that of the yam which
has an efficiency of 0%, in fact, it is the most inefficient
product maybe due to the archaic way it is grown. Yam is
predominantly grown in highland areas where the other
products have very high efficiency scores. Would farmers
have preferred some kinds of products to others?
The DEA method gives efficiency scores that do not
exceed 65%. Taking into account the constant returns to
scale, we can see that efficiency scores are around 60%,
whereas those of the variable returns to scale do not
exceed 50%. This second consideration shows a totally
inefficient agriculture throughout the reporting period of
this research. This vision of the Togolese agriculture can
be explained by the youth’s widespread lack of interest in
agriculture, which means that the resources (labor and
land) are available but poorly used. However, the
opposite view, which considers constant returns to scale,
goes with the SFA vision of the Togolese agriculture.
COMPARISON AND DISCUSSION OF RESULTS
The efficiency estimate of the Togolese agriculture using
the SFA and DEA methods identified different and
sometimes contradictory results. However, the SFA
method showed that the Togolese agriculture, with or
without technical progress, is efficient regarding the
Cobb-Douglas and Translog functions (Table 5).
However, the DEA method revealed conflicting results
depending on whether we put ourselves in a context of
constant or variable returns to scale. With the constant
returns to scale, the efficiency score is 0.612 because
there is no huge wastage. This means that the Togolese
Helali et al.
agricultural resource management is acceptable. With
non-constant returns to scale, the efficiency score is
0.458. This clearly means that the management of the
Togolese agricultural wealth has failed. The differences
between the SFA and DEA are based on the concept that
these approaches have some distance from the efficiency
frontier.
The DEA method ignores the measurement errors.
Therefore, the whole distance from the frontier is
considered inefficiency. On the other, the SFA method,
which considers measurement errors, seems to be more
realistic. It is noteworthy that the SFA method is more
efficient for theoretical studies, whereas the DEA is very
useful for practical or empirical studies. Therefore, the
conclusion drawn is that, in general, the Togolese
agriculture is inefficient like the finding of Agossou
(2009).
The general findings show that the agricultural labor in
Togo is shrinking the cultivated is increasing nonchalantly
and production tends to stagnate. Instead of announcing
the death of the Togolese agriculture, these indices are
rather encouraging because it is easily seen that the
increase of agricultural labor in Togo will necessarily
propel agricultural production. We should not either
declare victory because the results via the DEA analysis
are inconsistent with the idea of a promising Togolese
agriculture. All we can keep from this analysis is that
agriculture in Togo is promising and all the efforts made
to increase awareness, investment and innovation in this
area will pay off.
Conclusion
The empirical study of the Togolese agriculture efficiency
gave results that defy the prejudices about agriculture.
Beyond the pessimistic views that discourage investors,
this study revealed a reality quite opposite to the
widespread ideas about the Togolese agriculture and
Africa in general. The stochastic frontier analysis method
shows that the Togolese agriculture is weakly efficient.
This observation was affirmed by the results of the DEA
method. However, we prefer the SFA which shows that
the Togolese agriculture is generally better efficient. By
referring to the DEA, which is more empirical than the
SFA, we will find ourselves forced to consider that the
Togolese agriculture is globally inefficient.
Conflict of Interest
The authors have not declared any conflict of interest.
3145
REFERENCES
Agossou GT (2009). Evaluation de l'efficience technique des
exploitations rizicoles du périmètre irrigué de mission-tové. Université
de Lomé (Togo) - Ingénieur agro-économiste.
Danklou D (2006). L'agriculture du Togo. Institut Supérieur d'Agriculture
de Lille. http://dodjidanklou.free.fr/docpdf/agriculture_du_togo.pdf.
Djokoto JG (2012). Technical Efficiency of Agriculture in Ghana: A Time
Series Stochastic Frontier Estimation Approach. J. Agric. Sci. 4(1),
www.ccsenet.org/jas.
Eric H, Florian P, Arnaud S (2004). Translog ou Cobb-Douglas, le rôle
des durées d'utilisation des facteurs. Banque du Canada, no. 200419.
Farrell MJ (1957). The Measurement of Productive Efficiency. J. Royal
Stat.
Soc.
Series
A,
120(3):
253-290.
http://dx.doi.org/10.2307/2343100
Johnston BF, Mellor JW (1961). The Role of Agriculture in Economic
Development. Am. Econ. Rev. 51:566-93.
Kuznets SS (1968). Toward a Theory of Economic Growth with
Reflections on the Economic Growth of Nations. Norton, New York
library, P. 429.
Levin A, Lin CF, Chu CSJ (2002). Unit Root Test in Panel Data:
Asymptotic and Finite Sample Properties. J. Economet. 108(1):1-24.
http://dx.doi.org/10.1016/S0304-4076(01)00098-7
Lewis WA (1956). Theory of Economic Growth. George Allen & Unwin
Ltd. Great Britain, edition used Unwin University Books, ninth
impression, ISBN 0 04 330054 5.
McCoskey S, Kao C (1998). A Residual-based test for the null of
cointegration in panel data. Economet. Rev. 17:57-84.
http://dx.doi.org/10.1080/07474939808800403
Pedroni P (1999). Critical values for cointegration tests in
heterogeneous panels with multiple regressor. Oxford Bulletin of
Economics and Statistics, Special : November, pp. 653-669.
http://dx.doi.org/10.1111/1468-0084.61.s1.14
Somayeh MC, Ghasem T, Shaghayegh L, Sanaz MC (2012). Capacity
utilization of buyer-supplier relationships. Indian J. Sci. Technol. 5(9)
ISSN: 0974- 6846, September.
Thierry P (2010). Etude sur la pénurie de la main d'œuvre agricole au
Togo et au Bénin. Brücke Le pont/ CRISTO. http://www.brueckelepont.ch.
Yorgason VW (1974) Agriculture in Economic Development: The
Competitive Approach Versus Soviet Control. Can. J. Agric. Econ.
Rev. canadienne d'agroeconomie 22(1):84–85, February.
Vol. 9(42), pp. 3146-3155, 16 October, 2014
DOI: 10.5897/AJAR2014.8806
Article Number: CD9946F48071
ISSN 1991-637X
Copyright © 2014
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Full Length Research Paper
One and one half bound dichotomous choice
contingent valuation of consumers’ willingness-to-pay
for pearl millet products: Evidence from Eastern Kenya
Okech, S.O.1*, Ngigi, M.1, Kimurto, P. K.2, Obare, G.1, Kibet, N.3 and Mutai B. K.1
1
Department of Agricultural Economics and Agribusiness Management, Egerton University, Kenya.
2
Department of Crops, Horticulture and Soil Sciences, Egerton University, Kenya.
3
Department of Agricultural Sciences, Kisii University, Kenya.
Received 23 April, 2014; Accepted 16 September, 2014
Pearl millet is the fourth most important cereal after maize, rice and sorghum in terms of cultivation and
production in the tropics, yet the least traded of all cereals in Kenya. New pearl millet varieties
(KAT/PM1, KAT/PM2 and KAT/PM3) were introduced in response to low yield and birds’ menace that
was wiping out the traditional pearl millet varieties in Kenya. Despite this, limited information exists on
consumers’ willingness to pay and the determinants of willingness to pay estimates for these newly
introduced varieties products. This study was undertaken to analyze consumers’ willingness to pay and
the determinants of willingness to pay estimates for these pearl millet products. Results showed that
most consumers (70%) were willing to pay a premium price with the mean willingess to pay off 42%
over finger millet market price. Age, number of children below 12 years in a household, gender of
household head, income and awareness levels were the important factors that positively influenced
consumers’ willingness to pay premium prices.
Key words: Pearl millet, new varieties, willingness- to-pay, consumer, Kenya.
INTRODUCTION
Pearl millet (Pennisetum glaucum (L.) R. Br.) is the fourth
most important cereal after rice, maize and sorghum in
terms of cultivation and production in the tropics. It is
estimated that over 11.36 million tons of pearl millet
grains are produced in 17.4 ha globally (FAO, 2003). In
Kenya, for instance, the areas under pearl millet have
reduced from 100,143.9 ha (2011) to 93,310 ha in 2013.
Areas that have experienced this drastic fall in hectares
under pearl millet include: Eastern province (Tharaka,
Mbeere, Mwingi, Kitui, Makueni districts) and Rift Valley
province (Baringo, Elgeyo-Marakwet and West Pokot
districts) (GoK, 2007; KARI, 2008; MoA, 2008). Pearl
millet yields also have been declining since 1980 from
1,610 kg ha-1 to an estimated 200-800 kg ha-1 in 2008.
The current yields per ha is low against the estimated
-1
global potential of 1,500 to 3,000 kg ha (KARI, 2007;
GoK, 2007; KARI, 2008; MoA, 2008).
In terms of consumption, pearl millet has several merits
in the rural household food baskets. It contains high level
-1
of protein (up to 12%), energy (3600 K cal kg ) and a
*Corresponding author. E-mail: [email protected], Tel: +254724855300. Fax: +25451 61576.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
Okech et al.
balanced amino acid profile making it a cheaper source
of grain iron (Fe) and zinc (Zn) (Parthasarathy et al.,
2006; ICRISAT, 1996). In addition, it is a staple diet for
over 500 million households who are mainly small scale
farmers living in arid and semi-arid lands. However, in the
past three decades, its consumption pattern has been
declining partly due to the changing consumers taste and
preference, diseases (leaf blight and rust) and birds’
damage. Moreover, pearl millet industry has been facing
numerous constraints which include: poorly developed
and fragmented marketing channels with weak value
chains, high assembly and processing costs,
uncompetitive grain prices which collapse during
harvesting season, lack of market information, limited
processing facilities, in-adequate grain supply all year
round and high cleaning costs among other several
factors (FAO, 1996; Rohrbach, 2004). According to Gulia
et al. (2007), in the USA market, pearl millet has no
minimal set price; Eastern Kenya is not an exception to
such marketing risks. These challenges have not only
limited farmers’ interest but have also threatened to wipe
pearl millet out of farmer’s fields despite local
communities’ interest. In fact, it is reported that most
processing companies in Kenya (Kirinyaga Millers
Limited, Kabansora Millers Limited, Proctor and Allan
Limited) have lost contact with local farmers because of
unreliable supply and poor quality grains. Data showed
annual imports from Tanzania and Uganda for industrial
processing as 1,560 metric tons (United States Agency
for International Development, 2010). It is possible that
pearl millet might be lost completely from farmers fields
unless serious intervention is put in place.
As part of the intervention, the government of Kenya
has put more emphasis on promoting pearl millet value
addition to improve its competitiveness, acceptability and
food security (GoK, 2003). In addition, from 1994 to 2007,
the government of Kenya and International Fund for
Agricultural Development (IFAD) initiated a US $ 15.5
million Eastern Province Horticulture and Traditional
Food Crops Project to improve incomes and food security
for small scale farmers’ (Karanja et al., 2009). To further
promote pearl millet, International Crops Research
Institute for Semi-Arid and Tropics (ICRISAT),
International Sorghum and Millets (INTSORMIL) and Bill
and Melinda Gates Harnessing Opportunities for
Productivity Enhancement (HOPE) project have
developed interventions such as breeding, distribution of
improved varieties and promotion of resource
conservation and management to address productivity,
profitability and marketing challenges of dry land cereals
(Karanja et al., 2009). HOPE project is currently
promoting high yielding, low soil fertility resistant and
disease tolerant pearl millet varieties (KAT/PM 1,
KAT/PM 2 and KAT/PM 3) in the market. It is expected
that the small scale farmers will adopt these new varieties
in order to improve their food security situation and
income levels through sale of surplus. These efforts are
3147
in line with Kenya’s vision 2030 of promoting economic
development and food security amongst households
residing in arid and semi arid lands. However, a keen
look at these past efforts in addition to a review of earlier
research work reveals a strong focus on pearl millet
production and value addition.
Therefore, studies on pearl millet products demand and
consumption have received a slight or no research
consideration which have resulted in inadequate
knowledge on consumer attributes and willingness-to-pay
(WTP); that may hamper production optimization and
marketing of pearl millet products in Kenya. Therefore,
knowledge of consumers’ attributes and willingness to
pay for products made out of these introduced pearl millet
varieties are necessary for researchers to improve on the
already produced pearl millet hybrids. Moreover,
information on pearl millet products consumption may
lead to the development of pearl millet products meeting
specific demands of consumers. The objective of this
study was to find out if consumers were willing to pay
premium prices for these pearl millet products and which
factors would influence their willingness to pay for the
pearl millet products made out of these varieties.
Understanding of consumers’ willingness to pay is
important to farmers as they will benefit from having a
better understanding of the value of their products and
are thus be in a position to determine how to maximize
their profits. Companies, on the other hand, may benefit
by being able to determine the economic gain or loss
associated with investment they make in value addition.
MATERIALS AND METHODS
Study area
This study was undertaken in Mbeere district, Kenya due to its
favourable climatic conditions (LM3 and LM4) necessary for pearl
millet production, presence of numerous small scale pearl millet
processing units and the long history of pearl millet production. The
district covers a total area of 2,097 KM2 and lies between latitude
0°
N and 0°
N; longitude 37°
E and 37°
E with an
altitude range of 500 to 1200 m above sea level on Tana River
basin with a slope moderated by Kiambere, Kiang’ombe and
Kianjiru Hills (Government of Kenya (GoK), 2005). The district
experiences bimodal and unreliable rainfall averaging between 640
to 1110 mm with most parts receiving less than 750 mm annually.
However, this unreliability of rainfall increases major crop failures
especially for maize.
It has two main growing periods: long rains of April - June and
short rains occurring between October-December with a growing
length of between 90 to 119 days. The economic activity of the area
is mixed farming with livestock production mainly composed of
indigenous species of cattle, goats and chickens while crop
production is limited to rain fed agriculture (GoK, 2005). The
average temperature ranges between 20 to 30°C and sometimes
rises above 30°C in March- the hottest month. The soils range from
chromic Cambisols to rhodic Ferralsols and Luvisols with varying
degree of stoniness, rockiness and soil depth (GoK, 2005). These
soils are infertile and are highly sensitive to erosion as a result of
surface sealing and hard pan setting.
3148
Afr. J. Agric. Res.
Data description
This study was undertaken between August and September, 2012
– harvesting period to minimize variation in the consumption
patterns. A two stage sampling technique was used to select 100
consumers for the study. In the first stage, purposive sampling
technique was used to select Mbeere district due to its pearl millet
history, presence of numerous small scale pearl millet processing
units and the agro-ecological zones (LM3-LM4) necessary for pearl
millet growing. In the second stage, a random sampling technique
was used to select 100 consumers within the peri-urban areas of
Siakago, Ishiara and Kiritire towns. To improve on questionnaire
validity and content in tangent with study objectives, pretesting was
done. Important data to the study included: the level of consumer
awareness, socio-economic characteristics of pearl millet
consumers, and the willingness to pay for new pearl millet products.
Specifically, socio-economic factors considered important for the
study included: income level of household head, number of children
below 12 years, educational level of the household head, gender of
household head/decision maker, employment status of household
head, age of household head, level of awareness of household
head of pearl millet and if a household have heard of the new pearl
millet products before the study.
The model specification
In estimating consumers’ willingness to pay for novel goods in the
absence of a real purchasing power, and in situations where
primary data sources are available like in this study, contingent
valuation methodologies are commonly applied (Stevens et al.,
2000; McCluskey et al., 2001; Lusk and Hudson, 2004; Lin et al.,
2005).
In that regard, a Contingent Valuation (C.V.) methodology
following a closed ended survey format was applied in this
research. This survey will only provide meaningful results if it is
grounded on consumer maximization theory. This theory holds that
consumers maximize utility subject to budget constraints. It was
assumed that the consumers would choose a bundle of pearl millet
products giving him high satisfaction levels. The choice of C.V.
methodology was also based on the assumption that it exactly
mirrors the value of non market goods in the absence of a real
purchasing power. This is because it constructs a hypothetical
market which is not only structured but also realistic by ensuring
respondents’ are faced with a well defined situation contingent on
the event occurrence. Although, opponents of C.V. have punched
holes on its reliability, the selection of an appropriate survey and
elicitation methodology is capable of reducing or minimizing these
preconceptions. In this study, a modified dichotomous elicitation
technique - an extension of discrete choice with follow up approach
was applied (McCluskey et al., 2001; Lin et al., 2005; Cooper et al.,
2002).
In this technique, percentage bids were pegged on price
differentials and the first bid (B1) was assumed to be equivalent to
KES 100 to indicate price equality between pearl millet products
and finger millet products to reduce starting point bias. The second
bid (B2) or the premium bid was contingent to the first bid and was
assigned to a consumer only if he was willing to buy at a price
higher than initial bid amount. The second bid (B 2) and the initial bid
(B1) allows for setting of the lower and upper bounds on the
unobservable consumers true willingness to pay for products. If the
consumer responded to the first question with a ‘yes’, then he was
willing to purchase pearl millet product at a premium price. From
this, a follow up question with varying bid levels was given. From
these iterations, three possible outcomes of a semi-doublebounded model was obtained: ‘yes’ ‘yes’; ‘yes’ ‘no’ and ‘no’. A ‘yes’
‘yes’ indicated a consumers willing to purchase pearl millet product
at a premium; ‘yes’ ‘no’ indicated a consumer as having
intermediate willingness to pay while a ‘no’ indicated customers non
willingness to purchase pearl millet products at a premium.
Therefore, discrete outcomes (Dg) of the above bidding processes
can be written as (Rodríguez et al., 2008; Lin et al., 2005;
McCluskey et al., 2001):
Dg =
(1)
From the discrete outcomes above, group 1 represented individuals
not willing to purchase at any discount and are considered to have
the lowest WTP; group 2 represent intermediate willingness to pay
and finally, group 3 represented those who did not require any
discount but accepted to pay the highest premium. Therefore,
assuming a linear function of an individual i WTP for new pearl
millet products can be summarized as:
εi for i= 1, …n
Where
is the ultimate bid an individual i faces;
(2)
is a column
vector of observable socio- demographic characteristics of an
individual (age, income, employment status, gender of household
head, and the number of children below 12 years in a household);
are the parameters to be estimated;
and εi are
assumed to be linear in parameters for all individuals, with the error
term being independent and identically distributed with a mean zero
and a variance
. Under the above assumptions, the individual
choice probability may be stated as in McCluskey et al. (2001):
Prob. (D=J) =
(3)
The likelihood function can therefore be stated as below:
(4)
Where
the
is the indicator function for even k and
denoting
alternative occurrence. In the empirical implementation, G
may be defined as a standard logistic distribution function with
a mean of zero and standard deviation
. The
parameters of the function to be estimated using maximum
likelihood function are
. The value of (rho) ranges
between 0and 1 and should be positive to form downward sloping
S-curve as a negative value would contradict economic theory.
Finally, the mean WTP is derived as a ratio of
through
restricting variables coefficients to zero in parameter estimation with
the exception of the random bid (Rodríguez et al., 2008; Lin et al.,
2005; McCluskey et al., 2001). The final mean WTP is written as:
E (WTP,
,
, β, z) = α +λ z i )/
(5)
Okech et al.
REGRESSION MODEL
In understanding the factors affecting consumer’s willingness to pay
premium price, a logit model was adopted. The choice of this model
was guided by the discrete structure of WTP questions used in this
study. A logit model is initially set up in the form of a latent
regression with Equation (6) as its starting point;
(6)
In the Equation (6),
represent unobserved portion while the
observed section is represented as (Green, 2006):
(7)
In the Equation (7) above,
’s are known parameters under
estimation in conjunction with parameter
with an error term
normally distributed across all observations. Therefore, restricting
mean and the variance of the error term to 0 and 1, the below
probabilities are attained (Pishbahar et al., 2013);
(8)
For the above probabilities Equation (8) to be positive, the following
conditions needs to be met;
In a logit model, coefficients and marginal effects of explanatory
variables on the probabilities are usually different. By undertaking
first order differentiation of Equation (8), the marginal effects of
changes in the explanatory variables can be obtained as shown in
Equation (9) (Pishbahar et al., 2013;
(9)
The empirical specification of the final model for estimating factors
of WTP is shown below.
(10)
3149
In estimating factors affecting consumers WTP, LimDep version 7.0
software was used. The model significance was verified through
Chi-square statistics computation by calculating the restricted and
unrestricted log likelihood functions.
DESCRIPTION OF LOGIT MODEL VARIABLES
The explanatory variables influencing consumers’ WTP premium
prices and therefore included in the above logit model were
obtained from review of relevant past researches. Although demand
for a product is usually affected by its price, in this study, only sociodemographic factors (age, household head, employment status,
income, gender of household head, education and presence of
children below 12 years in a family), awareness and whether a
consumer has ever heard of the pearl millet product or not were
included. These socio-demographic characteristics were assumed
to be similar to those affecting consumer expenditure and analysis
for local products. The inclusion of awareness and whether a
person has ever heard of pearl millet products were based on an
assumption that not all consumers perceive quality in a uniform
manner despite their objectivity in the measurement of utility. In
fact, there exist possibilities that some product quality may yield
negative or positive utility to consumers.
The presence of children below 12 years in a household was
found a direct influence between consumers’ WTP and beef
products (Loureiro and Umberger, 2003). Gao et al. (2011) also
observed a positive linkage between WTP and presence of children
below 12 years in a family for fresh citrus consumers. And because
these products were considered high quality goods, we also
hypothesized pearl millet to be in this group. Therefore, a positive
relationship was expected (Table 1). Kimenju and De Groote (2005)
observed a positive connection between WTP and maize
(8)
consumers’ income levels. Also, Loureiro and Umberger (2003)
found a positive link between income levels and WTP amongst beef
consumers. In this study, a positive association was also expected.
Govindasammy et al. (2006) acknowledged a positive
relationship between consumer level of awareness and WTP. In
addition, Bate et al. (2007) observed a negative association
amongst organic foods consumers and their WTP. These results
offer divisive empirical evidence and as a result, we hypothesized
that it could take either a positive or negative sign. Owusu and
Onfori (2012) observed a direct relationship between level of
education and WTP for organic product consumers. Budak et al.
(2010) also observed a positive relationship between the level of
education and Turkish livestock producers WTP. In this study, we
believe that highly educated consumers consider pearl millet
products as a poor man’s food and therefore would not associate
with it. And as a result, a negative relationship between educational
level and WTP was eminent.
Carpio and Isengildina-Massa (2009) found out that female
headed households were more willing to pay premium prices than
male counterparts to obtain animal products. Haghiri and
McNamara (2007) noted that male headed households as willing to
pay premium than female headed households for pesticide free
products. Because of the inconclusiveness of discussion in this
variable, a positive or negative relationship was hypothesized.
Employment is a source of income and exposure to current
information. Therefore, consumers who were in employment were
expected to be well exposed and would buy pearl millet products
due to its health benefits and so a positive relationship was
anticipated.
The age of a household head affects his/her purchase decision
making. We hypothesized that older household heads as less
willing to pay premium prices due to their low income levels.
Consumers are rational people and would want more out of less. In
that sense, we hypothesized that whether a household have ever
heard of pearl millet or not was positively related to his willingness
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Afr. J. Agric. Res.
Table 1. Nature and a priori expected signs of Logit analysis of the factors determining consumers’ WTP.
Dependent variable
WTP (Willingness to Pay)
Definition of variables
If respondent is WTP a price premium for pearl millet products
Expected sign
1-Yes; 0- Otherwise
Independent variables
Income (Income)
Children (Nochildren)
Education (EducLevel)
Gender (Gender)
Employment (Employstatus)
Age (AgeofHH)
Household head (HHhead)
Awareness (Awareness)
Heard (HeardProduct)
The monthly income level of the household head
The number of children below 12 years in the household
Highest educational level of the respondent
Gender of the household head
Is the household head employed
Age of the decision maker (years)
If the buyer is the head household
The household head level of awareness of pearl millet product
If the Household have heard of a pearl millet product
+
+
+/+
+
+/_
+
to purchase it at higher prices. Finally, we believed that whether a
consumer was a household head or not was positively related to
his/her WTP premium prices. This was because if purchaser was a
household, he was responsible for decision making exercise and
therefore he would be more willing to pay a premium than if
otherwise.
statistics confirmed that 55% of pearl millet consumers
were not in formal employment, 21% were employed on
part time basis while 18% were on full time employment.
Consumers’ willingness to pay for pearl millet
products
RESULTS AND DISCUSSION
Socio-economic
consumers
characterization
of
pearl
millet
A socio-economic characterization of pearl millet
consumers are shown in Table 2. Results showed that in
terms of education, majority of the consumers (52%)
were in primary school; 31% were with secondary
education; only 3% had university education while 5%
were of illiterate category. On average, therefore, there
was low level of literacy amongst the consumers and this
implies that consumers may not be in a position to
capture the benefits associated with pearl millet
marketing as they may be disadvantaged during
bargaining process (Table 2).
The number of years a person takes in performing any
marketing function directly influences his/her marketing
experience and thus his profit levels. Therefore, the more
experience a marketer is, the higher his understanding of
a marketing system, conditions and prices trends. From
the study, the average mean age of consumers was
45.42 years (Table 2) and this implies that majority of
pearl millet consumers were dynamic youths within the
economically active age bracket of 21 to 50 years and
thus they were able to take risks associated with
marketing. Therefore, these actors could make a
meaningful impact in the consumption of new pearl millet
products. Result further revealed that, 61% of pearl millet
consumers were males or male headed households
(Table 2). This might partly be attributed to the high
prevalence of HIV/AIDS menace in the area. Moreover,
Contingent valuation methodology is normally criticized
due to starting point bias and complication of the relevant
estimates of willingness to pay. Therefore, to reduce
these biases while simplifying consumer tasks in decision
making, a starting point price of KES 100 (US $1.18)1
was proposed. This price (KES 100) was the operating
market price for substitute products (finger millet)
commonly available in the Eastern Kenya markets during
the study period. Moreover, the choice of this substitute
product was also informed by our baseline survey
undertaken in 2011 in which producers reported that they
had replaced pearl millet with finger millet in their farms.
Indeed, this price was similar to a true Kenyan market
situation where consumers are usually faced with a fixed
price for a product and then required to make decision in
its purchase. With this background, consumers were
asked if they were willing to purchase pearl millet
products at KES 100 for a two kilogram tin.
Those respondents who gave a positive response to
the first question were further given the option of
selecting a bid2 amongst the 5 point bids of 10, 20, 25, 50
and 75% and the result is presented in Table 3. Although
all consumers were assumed to be rational in their
purchase decisions, they exhibit varying WTP for given
products. And as a result, 5 point bids (Table 3) were
randomly chosen to bracket their expected maximum
willingness to pay. These bids were assigned to a
Exchange rate – US $1 = KES 85
Bid – This the maximum amount of money a consumer would be willing to
pay to get a pearl millet product in the market
1
2
Okech et al.
3151
Table 2. A summary of pearl millet consumers’ socio-demographic characteristics.
Specific variables
Mean age
Educational level (%)
Description
years
Illiterate
Primary
Secondary
Tertiary
University
Total (%)
Gender (%)
Male
Female
61
39
100
Full time
Part time
Unemployed
Housekeeper
Retired
18
21
55
4
2
100
Total (%)
Employment status (%)
Total (%)
1
Standard deviation.
Table 3. Distribution of willingness-to-pay (WTP) responses.
WTP category (%)
10
20
25
50
75
Total
Consumers
1
45.4 (11.7)
5
52
31
9
3
100
Frequency
23
19
14
10
4
70
Percentage
32.86
27.14
20.00
14.29
5.71
100.00
respondent, and they were only given one second
chance to bid for these percentages. The random
selection and distribution of WTP percentage bids was
assumed to offer excellent market information for pearl
millet market actors.
Distribution of willingness-to-pay responses
The percentage of the respondents who indicated
positive WTP decreased with increase in premium
offered implying that an increase in price decreases
demand levels (Table 3). Out of the 70 positive
respondents, 32.9% were willing to pay 10% while only
5.7% were willing to pay 75% price premium for the new
pearl millet products. This implied that most consumers
did not believe that these new pearl millet products could
attract higher prices or be used as a raw material in
making products described by the study instrument.
Similar studies by Shukri and Awang-Noor (2012) in
Malaysia reported 53.4% of their respondents as WTP
RM 500 in comparison to 90.3% of respondents who
were WTP RM 100 to acquire certified timber products. In
addition, Kimenju and De Groote (2005) in Kenya found
out that 50.3% of the respondents were willing to pay 5%
while 39% were willing to pay a premium of 50% above
the normal maize prices. Gunduz and Bayramoglu
(2011), on the other hand, observed 81% of Turkish
organic chicken consumers’ were willing to pay price
premiums. Out of these, 28, 29 and 10% were WTP less
than 5%, 6 to 10% and more than 10% price premium
respectively above the regular chicken prices.
Estimate of consumers mean willingness-to-pay
Based on the result from contingent valuation method,
the mean WTP was estimated and presented in Table 4.
The result indicated that respondents were on average
willing to pay Kshs.142 representing a premium of 42%
over the normal price of Kshs 100 for similar finger millet
product. In general, consumers showed a positive
willingness to pay for new pearl millet products. These
findings are similar to those of Padilla et al. (2007) who
estimated a mean WTP for Chilean consumers at 39%
for officially certified labels on traditional food products.
On the other hand, Nepal consumers were willing to pay
between 40 to 60% for labeled tomatoes (Bhatta et al.,
2010). Loureiro and Umberger (2003) also observed that
USA consumers were willing to pay 38 and 58% for
certified steak and Hamburger above the initial given
prices respectively. It is important to note that the true
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Afr. J. Agric. Res.
Table 4. Parameter estimates of mean WTP model.
Variable
Constant (α)
Bid (ρ)
Mean WTP (α/ ρ)
Coefficient estimate
9.235
0.065
142.077
Standard error
1.662
0.013
P- value
0.000
0.000
Number of observations = 100; Log likelihood = -63.862.
Table 5. Empirical result from Logit model and their marginal effects.
WTP
HHhead
AgeofHH
Gender
EducLevel
NoChildren
Employstatus
Income
Awareness
HeardProduct
Constant
Coefficients
-0.555
0.080
1.252
-0.102
0.558
0.238
1.029
1.351
0.229
-10.540
Standard error
0.716
0.029
0.728
0.348
0.244
0.277
0.388
0.420
0.455
3.136
z
-0.78
2.76
1.72
-0.29
2.29
0.86
2.65
3.22
0.50
-3.36
P>|z|
0.438
0.006*
0.086***
0.769
0.022**
0.390
0.008*
0.001*
0.615
0.001
Marginal effects
-0.056
0.008
0.127
-0.010
0.057
0.024
0.105
0.138
0.023
-
Statistics: Number of observations = 100; Prob.> chi square = 0.0000; Log likelihood = -33.660; Pseudo R-squared = 0.4014; Likelihood
ratio test of zero slope coefficients= 45.15; * significant at 1%; ** significant at 5%; *** significant at 10%.
position of the mean WTP might be lower than our mean
WTP estimate partly due to the hypothetical nature of
survey data used in the contingent valuation
methodology. Such low levels of WTP (6-10 percent of
regular price for equivalent products) have been
observed by Boccaletti and Moro (2000) amongst Italian
genetically modified consumers. Therefore, these results
should be interpreted with caution.
Factors affecting consumer’s willingness-to-pay
A total of 100 respondents from Mbeere district were
interviewed to support the analysis of the factors affecting
consumers WTP. The main variables hypothesized to
influence consumers WTP and used in logistic regression
analysis included: a household head monthly income
levels in Kshs (Income), level of respondents awareness
of new pearl millet product (Awareness), if the
respondents have ever heard of new pearl millet products
(HeardProduct), total number of children below 12 years
in a given household (No Children); Age of the buyer or
household head (AgeofHH), the highest number of years
a buyer/ household head have been in school
(EducLevel) and the employment status of a household
head/buyer (employment status). In addition, dummy
variables like if the respondent was a household head or
otherwise (HHhead) and whether the household head
was male or female (Gender) were also included in the
study (Table 5).
The findings of the maximum likelihood estimate for
WTP a premium price is presented in Table 5. The model
revealed a McFadden’s R2 statistic of 0.40 which is a
reasonable value considering the cross sectional nature
of data and therefore correctly predicted the actual choice
of WTP premium by consumers (Kennedy, 1994;
Govindasamy and Italia, 1999). Moreover, the calculated
Chi square statistic of 0.000 was statistically significant
indicating the statistical significant of the model in
explaining the result. In general, five out of nine
hypothesized factors were significant at 1, 5 and 10%.
From empirical result, the coefficient of households with
children below 12 years old is positive sign and
significant at 5% level (Table 5). This means that
households with children below 12 years old were 5.6%
more WTP premium prices than those without these
children. This is because, households with children are
more concerned with the nutritional values of food they
give to children and pearl millet is perceived as one such
category of food. This empirical result mirror those of Gao
et al. (2011) on USA fresh citrus consumers where he
observed that households with children below 12 years
tended to be in a pro quality cluster than perfectionist
clusters. Because parents were more focused on fruit
quality, they were willing to pay premium prices for quality
fresh citrus fruits compared to those without children. In
Turkey, Budak et al. (2010) observed those producers
with larger herd sizes as more willing to pay premium
prices than those with limited herd sizes to receive
extension services due to their contribution to household
Okech et al.
income. In USA, Loureiro and Umberger (2003) observed
a contradicting result as households with children
portrayed a negative correlation with willingness to pay
for beef products. Bhatta et al. (2010) also noted a
negative relationship between family size and willingness
to pay for inorganic vegetables like tomatoes. This they
attributed to the fact that a larger family size means more
expenditure on foods and to cut down on these
expenditures, they need to opt for cheaper food types.
Educational level attained by a given household directly
affects a consumer’s willingness to pay levels. Access to
better education enhances understanding of nutritional
information; therefore, respondents with education should
possess higher WTP. However, from empirical result
(Table 5), the coefficient of the variable representing
educational level was negative and had no significant role
in influencing consumers’ willingness to pay premiums.
Those with higher education were 1.0% less likely to pay
premium prices compared to those with low education.
This result is consistent with the priori assumption that as
a person stays more in school; its willingness to pay for a
premium price reduces. This is true because educated
consumers can think and judge between good and
healthy food and which is not. Secondly, they might
expect that it is government duty to provide these
products free of charge and thus their unwillingness to
pay premium prices. In addition, educated consumers are
more likely to be in off farm employments and therefore
might over emphasize the need for produce safety
standards. These findings are similar to Budak et al.
(2010) who observed that highly learned Turkish
producers’ unwillingness to pay premium prices for
extension services. Bhatta et al. (2010) also observed a
positive and significant result with Nepal inorganic
vegetable consumers. However, they negate those of
Owusu and Onfori (2012) findings that well educated
Ghanaian organic food consumers’ willingness to pay
higher premiums compared to less educated consumers.
In addition, they contrast studies of Loureiro and
Umberger (2003) findings that a unit increases in the
level of education increases the level of US consumers
WTP premium for certified Hamburger.
According to economic theory, as household income
increases, the probability of household willingness to pay
also increases. From our empirical result (Table 5), a
household’s monthly income levels had a positive sign
which was significant at 5% level with willingness to pay
for new pearl millet products. A unit increase in a
household monthly income increases its willingness to
pay premium for new pearl millet products by 10.5%. This
is consistent with our earlier expectation that an increase
in a household income levels had a direct and a positive
effect on premium payment. These results are consistent
with Kimenju and De Groote (2005) observation that
Kenyan maize consumers with higher income levels
(greater than Kshs. 50,000) were willing to pay premium
prices than those with low incomes levels. Similarly,
3153
Owusu and Onfori (2012) recorded that Ghanaian
organic food consumers were willing to pay premium
prices as monthly income levels increases. In addition,
Govindasammy and Italia (2009) pointed out that USA
households with earnings less than USA $ 30,000 were
16% less likely to pay premium compared to those
earning over USA $ 70,000. Loureiro and Umberger
(2003) on the other hand observed that as income levels
of US households increases, their WTP premium prices
decreases. They attributed this to the fact that, wealthy
consumers in the USA considered their meat supply as
safe and therefore no labeling was necessary. Boccaletti
and Moro (2000) also observed a positive relationship
between an individual’s income level and his willingness
to pay. For instance, individuals with high income levels
translated greater benefits accruing from genetically
modified (GM) foods to money equivalent.
In terms of gender, our empirical results showed that
the coefficient was positive and statistically significant at
10% level with consumers’ willingness to pay premium
prices (Table 5). This indicates that male consumers
were 12.7% more likely to pay premium prices for new
pearl millet products compared to female consumers.
Similar finding was also reported by Haghiri and
McNamara (2007) pointed out in Canada that male
consumers’ were more willing to pay premium prices than
female counterpart for pesticide free produce. In addition,
In China’s urban cities, Lin et al. (2005) found out that
male Chinese consumers were more willing to purchase
Soybean compared to female consumers. However, in
USA, Govindasamy and Italia (1999) observed that male
consumers were 12% less likely to pay 10% premium to
acquire an organic produce. In South Carolina- USA,
Carpio and Isengildina-Massa (2009) observed female
consumers’ as 44% more willing to pay an additional
price to obtain animal products compared to male
consumers. Loureiro and Umberger (2003) also observed
female USA households as more willing to pay a
premium for mandatory country of origin labeling
programs.
Household heads are decision makers in a family, and
therefore their age levels are important in making a
purchase decision. From the a priori assumption, it was
hypothesized that older household heads might not be
willing to pay premium prices as they are in their
retirement ages with little income. However, from the
empirical results (Table 5), the coefficient for the age of
household head has a positive and significant sign at 1%
level. This indicates that if a respondent was old, they
were 0.8% more likely to pay higher price for the new
pearl millet products compared to when the respondent
was young. This implies that older consumers were more
motivated in purchasing new pearl millet products than
younger consumers. This might be attributed to the long
history of pearl millet in Mbeere district and so they had
excellent knowledge in its benefits and uses.
Access to information increases the level of consumers’
3154
Afr. J. Agric. Res.
awareness by eliminating consumer suspicion. According
to economic theory as level of consumer awareness
increases, the higher their willingness to pay premium
prices. From Table 5, the coefficient of consumer level of
awareness was positive and significant at 1% level with
willingness to pay. Therefore, consumers who were
aware of the benefits of consuming pearl millet products
were 13.7% more likely to pay a premium than those with
limited information about it. This implies that consumers
aware of healthy foods are motivated in purchasing of the
safe and healthy food items from the market. Bate et al.
(2007) on USA consumers’ willingness to pay for multi
ingredient processed organic foods observed no
significant difference between aware and not aware
consumers about national organic products seal. Pokou
et al. (2010) on Ivory Coast farmers’ willingness to
contribute to Tsetse fly control observed that farmers with
better information on Tsetse fly symptoms and control
increased their labour participation in its control
mechanisms. Boccaletti and Moro (2000) also noted
individuals with proper information had confident
concerning genetically modified foods which therefore
increases their willingness to pay.
In addition, Govindasamy et al. (2006) on Northeastern
USA organic produce consumers concluded that higher
price premium paid by consumers are a direct
relationship with the level of awareness of the product’s
usefulness. Bhatta et al. (2010) also observed a positive
and significant result with Nepal inorganic vegetable
consumption. However, other variables like household
head (HHhead), employment status (Employstatus) and if
consumers have ever heard of new pearl millet products
produced a statistically insignificant results (Table 5).
strategies to benefit from this niche market. Finally,
empirical results also indicated that not all consumers
were willing to pay equal price premium for pearl millet
product. As empirical results showed that not all
consumers were willing to pay equal price premiums for
pearl millet products, marketers should classify
consumers according to their willingness to pay
percentage estimates and adopt matching marketing
strategies to derive maximum benefit from these
consumers.
Conflict of Interest
The authors have not declared any conflict of interest.
ACKNOWLEDGEMENTS
This work was supported by African Economic Research
Consortium (AERC) through Collaborative Masters in
Agricultural and Applied Economics (CMAAE) program.
The author is grateful for the Association for
Strengthening Agricultural Research in East and Central
Africa (ASARECA) for funding part of our data collection
exercise
through
Harnessing
Opportunities
for
Productivity Enhancement (HOPE) project. The authors
would also like to acknowledge insightful comments from
the two anonymous reviewers which greatly improved the
quality of this paper. The authors take all the
responsibility for any errors herein this documents.
REFERENCES
CONCLUSIONS AND POLICY RECOMMENDATIONS
Due to the importance pearl millet plays in rural
households food baskets, this study examined whether
consumers are willing-to-pay any additional amount if
products of new pearl millet varieties were availed to
them. Our results do recognize that 70% of Kenyan
consumers were willing to show their appreciation for a
pearl millet product through premium payment of 42%.
Further analysis of data using a Logit model gave a
consistent result with regard to signs and significance of
coefficients. In terms of factors affecting consumers’
WTP, results showed that households with a monthly
income and children below 12 years and those with prior
knowledge of pearl millet were most likely to pay price
premium. Therefore, to boost profits from this potential
niche and lucrative market sector, these households
should be the prime target of marketers if they want to
retrieve maximum returns.
In order to harness the existing opportunities in pearl
millet market value chain, this study recommends the
following. First, fast food marketers should be familiar
with this price premium and adjust their marketing
Bhatta GD, Doppler W, Krishna-Bahadur KC (2010). Urban demand for
organic tomatoes in the Kathmandu valley, Nepal. Middle East J. Sci.
Res. 5(4):199-209.
Boccaletti S, Moro D (2000). Consumer willingness to pay for
Genetically Modified food products in Italy. AgBioForum 3(4):259267.
Budak DB, Budak F, Kacira ÖÖ (2010). Livestock producers’ needs and
willingness to pay for extension services in Adana province of Turkey.
Afr.
J.
Agric.
Res.
5(11):1187-1190.
Retrieved
from
http://www.academicjournals.org/AJAR
Carpio CE, Isengildina-Massa O (2009). Consumer Willingness to Pay
for Locally Grown Products: The Case of South Carolina. J. Agribus.
25(3):412–426.
http://dx.doi.org/10.1002/agr.20210
Cooper JC, Hanemann M, Giovanni S (2002). One-and-one-half-bound
dichotomous choice contingent valuation. Rev. Econ. Stat. 84(4):742750. http://dx.doi.org/10.1162/003465302760556549
FAO (1996). The world sorghum and millet economies: Facts, trends
and outlook. Food and Agriculture Organization. FAO. Rome.
FAO (2003). FAOSTAT. Food and Agriculture Organization of the
th
United Nations. Retrieved on 15 May 2011 from http//apps.fao.org
Gao Z, House LO, Gmitter FG Jr, Filomena MV, Plotto A, Baldwin EA
(2011). Consumer preferences for fresh Citrus: Impacts of
demographic and behavioral characteristics. Int. Food Agribus.
Manage. Rev. 14(1):23-39.
GoK (Government of Kenya) (2003). Economic recovery strategy for
th
wealth and employment creation: 2003 – 2007. Retrieved on 15
August 2012, from:
http://siteresources.worldbank.org/KENYAEXTN/Resources/ERS.pdf
Okech et al.
GoK (Government of Kenya) (2005). Mbeere district vision and strategy:
th
2005-2015.
Retrieved
on
10
March
2012,
from
http://www.aridland.go.ke/NRM_Strategy/mbeere.pdf
GoK (Government of Kenya) (2007). Mbeere District Development Plan,
2002- 2007, Government Printers, Nairobi.
Govindasamy R, Italia J (1999). Predicting willingness to pay a premium
for organically grown fresh produce. J. Food Distri. Res. 30(2):44-53.
Govindasamy R, DeCongelio M, Bhuyan S (2006). An evaluation of
consumer willingness to pay for organic produce in the Northeastern
U.S. J. Food Products Mark. 11:3–20.
http://dx.doi.org/10.1300/J038v11n04_02
Green W (2006). Econometric Analysis. Macmillan, NewYork, USA.
Gulia SK, Wilson JP, Carter J, Singh BP (2007). Progress in grain pearl
th
millet research and market development. Retrieved on 14 April
2012, from www.hort.purdue.edu/newcrop/ncnu07/pdfs/gulia196203.pdf
Gunduz O, Bayramoglu Z (2011). Consumer’s Willingness to Pay for
Organic Chicken Meat in Samsun Province of Turkey. J. Anim. Vet.
Adv. 10(3):334-340. http://dx.doi.org/10.3923/javaa.2011.334.340
Haghiri M, McNamara ML (2007). Predicting consumers’ acceptability of
pesticide-free fresh produce in Canada’s Maritime Provinces: A
Probit analysis. J. Int. Food Agribus. Mark. 19:4.
http://dx.doi.org/10.1300/J047v19n04_04
ICRISAT (International Crops Research Institute for Semi-Arid and
Tropics) (1996). The world sorghum and millet economies.
International Crops Research Institute for the Semi-Arid Tropics.
Patancheru 502 324, Andhra Pradesh, India: ICRISAT; and Rome,
Italy. FAO.
Karanja D, Kamau C, Muthoni L, Kaguma A, Kariuki CW, Kavoi J,
Ochieng A, Wafula WJ, Ariithi CCK, Kega V (2009). Improving Food
Security and Income of
Farmers in ASALs Through
th
Commercialization of Gadam Sorghum. Retrieved on 15 August
2012,
http://www.kari.org/kasal/docs/katumani_improving_food_security.pdf
KARI (Kenya Agricultural Research Institute) (2007). Annual report
2006. Kenya Agricultural Research Institute. KARI headquarters,
Nairobi.
KARI (Kenya Agricultural Research Institute) (2008). Annual report
2007. Kenya Agricultural Research Institute. KARI headquarters,
Nairobi.
th
Kennedy P (1994). A guide to Econometrics. 5 Ed., Cambridge,
Massachusetts, The MIT press.
Kimenju SC, De Groote H (2005). Consumers’ willingness to pay for
th
genetically modified foods in Kenya. 11 International Congress of
the EAAE (European Association of Agricultural Economists), The
Future of Rural Europe in the Global Agri-Food System,
nd
Copenhagen, Denmark. Retrieved on 2 February 2013, from
http://ageconsearch.umn.edu/bitstream/24504/1/pp05ki01.pdf
Lin W, Somwaru A, Tuan F, Huang J Bai J (2005). Consumers’
Willingness to Pay for Biotech Foods in China. Proceedings of the
American Agricultural Economics Association, Annual Meeting in
rd
Providence, R.I., July 24-27, 2005. Retrieved on 3 April 2012, from
http://ageconsearch.umn.edu/bitstream/19569/1/sp05li07.pdf
McCluskey JJ, Ouchi H, Grimsrud KM, Wahl TI (2001). Consumer
response to genetically modified food products in Japan. Retrieved
nd
on 22 May 2012, from:
http://impact.wsu.edu/publications/tech_papers/pdf/01-101.pdf
3155
Owusu V, Anifori MO (2012). Assessing consumer willingness to pay a
Premium for organic food product: Evidence from Ghana.
Proceedings of the International Association of Agricultural
Economists (IAAE) Triennial Conference, Foz do Iguaçu, Brazil.
th
Retrieved
on
16
November
2012,
from
http://ageconsearch.umn.edu/bitstream/123394/2/owusu%20and%20
anifori_IAAE_Brazil%202012.pdf
Padilla C, Villalobos P, Spiller A, Henry G (2007). Consumer preference
and willing to pay an officially certificated quality label: Implications
for traditional food producer. Agric. Técnica 67:300-308.
Parthasarathy R, Birthal BS, Reddy-Belum VS, Rai KN, Ramesh S
(2006). Diagnosis of Sorghum and Pearl millet grains based nutrition
in India. International Sorghum and Millets Newsletter 47:93-96.
Pishbahar E, Haghjou M, Hayati B, Mohammadrezaei R, Dashti G
(2013). Factors affecting consumers’ potential willingness to pay for
organic food products in Iran: Case study of Tabriz. J. Agric. Sci.
Technol. 15:191-202.
Pokou K, Kamuanga MJB, N’Gbo AGM (2010). Farmers’ willingness to
contribute to Tsetse and Trypanosomosis control in West Africa: The
case of Northern Côte d’Ivoire. J. Biotechnol. Agron. Soc. Environ.
14(3):441-450.
Rodríguez E, Lacaze V, Lupín B (2008). Contingent Valuation of
Consumers’ Willingness-to-Pay for Organic Food in Argentina.
Proceedings of the 12th Congress of the European Association of
th
Agricultural Economists (EAAE). Retrieved on 18 August 2012, from
http://ageconsearch.umn.edu/bitstream/43947/2/151.pdf
Rohrbach DD (2004). Improving the commercial viability of sorghum
and pearl millet in Africa. Series report. ICRISAT, Bulawayo,
th
Zimbabwe. Retrieved on 20 January 2012, from:
www.afripro.org.uk/papers/paper22rohrbach.pdf
Shukri M, Awang-Noor AG (2012). Malaysian consumers’ preference
and willingness to pay for environmentally certified wooden
household furniture. Pertanika J. Trop. Agric. Sci. 35(3):603–611.
United States Agency for International Development (USAID) (2010).
Staple foods value chain analysis: Country report- Kenya. United
th
States Agency for International Development. Retrieved on 24 June
2011, from:
http://www.competeafrica.org/Files/Kenya_Staple_Foods_Value_Cha
in_Analysis_Study_JANUARY_2010.pdf
Vol. 9(42), pp. 3156-3163, 16 October, 2014
DOI: 10.5897/AJAR2013.7982
Article Number: 8BB9C7448073
ISSN 1991-637X
Copyright © 2014
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Full Length Research Paper
Nitrogen application and inoculation with Rhizobium
tropici on common bean in the fall/winter
Antonio Carlos Rebeschini1, Rita de Cássia Lima Mazzuchelli1, Ademir Sergio Ferreira de
Araujo2 and Fabio Fernando de Araujo1*
1
UNOESTE, Agricultural Science Faculty , 19067-175, Presidente Prudente, SP, Brazil.
2
Federal University of Piauí, Agricultural Science Center, Teresina, PI, Brazil.
Received 26 September, 2013; Accepted 9 September, 2014
The objective of this study was to evaluate doses of nitrogen in the cultivation of bean under field
conditions, associated with seed inoculation. The study was conducted under field conditions in two
traditional bean cultivation in Paraná northern (Cafeara and Florestopolis).The experiment consisted of
plots with or without seed inoculation with Rhizobium tropici and use of five doses of nitrogen
coverage(0, 20, 40, 60 and 80 kg ha-1). Evaluations were made about the nodulation, N content in leaves,
shoot dry matter, N uptake, number of pods per plant and grain yield. The experimental design was
randomized blocks with ten treatments and four replicates. It was factorially arranged in five levels of
nitrogen with and without inoculation. In this study there was no response to inoculation in the
evaluation of the yield of bean in Cafeara and Florestopolis with different soil management. The
nodulation of bean with Rhizobium is reduced by fertilization with mineral nitrogen. The system with
tillage cultivation favored the development of bean during autumn-winter.
Key words: N2 fixation, Rhizobium spp., Phaseolus vulgaris.
INTRODUCTION
With an average yield of 951 kg ha-1 in 2011 Brazil
produced approximately 3.5 million tons of beans (IBGE,
2012). The cultivation of this legume is accomplished in
three seasons, the first being called "rainy season", the
second "the dry season" and the third "season of fall /
winter." The small bean producers have invested
frequently in the rainy season, due to the dependence on
weather conditions, while large producers invest in other
crops by the increased availability of irrigation (Person,
2012).
For production of protein-rich grain bean requires an
adequate supply of nitrogen (N), to care for their growth
as well as for the formation of pods and grains (Fancelli
and Dourado, 2007). The availability of mineral nitrogen
for plants is directly dependent on the continuous
decomposition of organic matter (N mineralization),
fertilization (Binotti et al., 2010) or biological N2 fixation
(BNF) made by symbiotic association with rhizobia roots
(Araújo et al., 2007). The need to improve productivity of
legumes as a global source of dietary protein, however,
has made it vital to understand the factors that influence
nitrogen fixation (Schulze, 2004).
The efficiency of BNF in bean is very variable and
under favorable environmental conditions can provide up
*Corresponding author. E-mail: [email protected]
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
Rebeschini et al.
3157
Table 1. Analysis of soil composition of the experimental area of Cafeara and Florestopolis.
Parameter
pH CaCl2
pH in SMP
Al
Organic matter
Ca
Mg
K
P
SO4
Mn
Fe
Cu
Zn
B
Units
-3
mmolc.dm
g.dm -3
mmolc.dm-3
-3
mmolc.dm
mmolc.dm-3
-3
mg.dm
mg.dm -3
mg.dm -3
mg.dm -3
mg.dm -3
-3
mg.dm
-3
mg.dm
to 80 kg ha-1 of N fixed (Melo and Zilli, 2009). The
application of 20 kg ha-1 of nitrogen at seeding associated
with rhizobia inoculation can provide input of 160 kg ha-1
nitrogen (Pelegrin et al., 2009). It is necessary to care for
the dose of N at sowing as studies have documented the
negative impact of high nitrate concentration in soil on
nodulation and nodule weight (Muller and Pereira, 1995).
The species of Rhizobium with higher prevalence in
symbiosis with bean show frequencies of 17,1% at
Rhizobium tropici, 35.9% Rhizobium etli, 32.5%
Rhizobium leguminosarum, 1.7% Rhizobium giardinii and
12.8% with distinct profiles of described species of bean
rhizobia, emphasizing the high genetic diversity of
rhizobia, including the appointment of new species
(Stocco et al., 2008).
The lack of response to inoculation of bean is usually
due to the inability of strains inoculated to compete with
native species for soil infection sites in roots (Ferreira et
al., 2011). The bean can respond similarly with and
without inoculation, indicating soils with high native
communities of rhizobia in the soil (Pelegrin et al., 2009).
There is a variability in responses to both bean rhizobial
inoculation and to nitrogen fertilization, mainly influenced
by the levels of soil fertility and other techniques used in
production systems, especially the use of irrigated
systems (Pelegrin et al., 2009). The efficiency of nitrogen
absorption is enhanced in these cultivation systems, with
this reducing production costs (Sant’ana et al., 2011).
In many regions of the world where common beans are
grown, nitrogen fixation is limited by unfavorable soil
conditions and temperature and water stress (Kabahuma,
2013). The tilling season and soil management contribute
decisively to obtain high yields in dry beans. Bean plants
grown in no-tillage has provided better conditions for
nitrogen use, possibly because of increased soil water
retention which may buffer temperature (Romanini Junior
et al., 2007). In this context the hypothesis of this study is
Cafeara
4.9
6.4
0
16.0
10.0
4.0
1.0
2.0
8.9
80.5
31.9
3.7
6.9
0.32
Florestopolis
5.0
6.8
0
11.0
7.0
3.0
0.9
11
6.2
15.8
15.1
2.5
1.2
0.29
that the climatic conditions and soil water availability,
found in the growing season of the third season (autumnwinter), are crucial for the evaluation of BNF. This work
aimed at the effect of rhizobia inoculation and application
of nitrogen in cover on bean cultivation in two distinct
areas, the autumn-winter season.
MATERIALS AND METHODS
Experimental site
The study was conducted in two areas of traditional cultivation of
bean in northern Paraná, Brazil in 2010. One area is located at
Cafeara (22° 46’22”S; 51° 43’18”W) altitude of 377 m. The other at
Florestopolis (22° 52’25”S; 51° 22’55”W) altitude 400 m. The soil in
both areas is classified as ultisol, emphasizing that the soil Cafeara
is under no-tillage (NT). The soil fertility analysis are presented in
Table 1. The experiment was conducted in the fall / winter season,
from April to August 2010.
Biological material
In the experiment was used bean genotype Carioca (IPR 139),
indeterminate growth. The inoculum used in the study was
commercial inoculant in peat formulation (Turfal, Curitiba, PR)
containing R. tropici, strain SEMIA 4088, the concentration of 1.0 x
109 viable cells per gram of product. The dosage used was that
recommended by the manufacturer of 100 g per 50 kg of seed. The
product was added to seeds with use of sugar adhesive solution
plus 10% (weight) immediately prior to sowing.
Experimental design
The experiment consisted of plots with or without inoculation of R.
tropici seeds and use of five N rates (0, 20, 40, 60 and 80 kg ha-1)
in cover effected at 20 and 40 days after plant emergency. A
completely randomized block with ten treatments arranged
factorially with four replications was used. The experimental unit
consisted of plots with six bean lines (5 m) spaced at 0.45 m
3158
Afr. J. Agric. Res.
Precipitation (mm)
300
CAFEARA
FLORESTOPOLIS
250
START
FINISH
200
150
100
50
0
J
F
M
A
M
J
J
A
S
O
N
D
Figure 1. Rainfall accumulation in 2010 in the experimental areas in Cafeara and Florestopolis.
between rows and a total area of 13.5 m² per plot.
(P0.05 and P0.01) and when found statistical differences were
applied to regression analysis doses nitrogen using the statistical
software SISVAR (Ferreira, 2000).
Cultivation
The plants was conducted in the field under natural conditions of
climate and soil. Based on the analysis of soil fertility was effective
at sowing fertilization with 250 kg ha-1 of NPK fertilizer (00-20-20),
and afterwards we made fertilization coverage with urea (45% N)
according to pre-established treatments. The seeds were sown in
April. The production system and plant management such as
control of pests, diseases and weeds were conducted in accordance with the detected events and technical recommendations for
culture in the region.
Number and weight of nodules, foliar N and biomass
For evaluation of nodulation and dry matter of the plant were
collected 10 plants per plot at the stage of full flowering (R6). To
determine concentration of nitrogen leaves were collected from the
middle third of 30 plants per plot. To count the number of pods per
plant were collected ten plants per plot at maturity stage (R9). The
analysis and quantification of shoot dry weight and dry weight of
nodules were performed in the laboratory after drying the material
on forced ventilation oven (60-70°C) to constant weight. The
analysis of leaf total N were performed according to methodology
(Malavolta et al., 1997).
Evaluation of yield
For the determination of final yield, the plants were collected from
two central rows of each plot, ignoring 0.5 m of each line at the
beginning and end, that after threshing and grain moisture
correction to 13% was calculated grain yield per hectare.
Statistical analysis
The data were subjected to analysis of variance (ANOVA) by F test
RESULTS AND DISCUSSION
Climatic conditions, monitored here as water availability
during the experiment period (Figure 1), showed in
Cafeara precipitation total of 140 mm, below the crop
necessity because there are reports that at 75 days cycle
development bean has one evapotranspiration 220 mm
(Silveira and Stone, 1979). In Florestopolis, there was
total precipitation of 108 mm. With respect to temperature
variation in both locations, there were minimum
temperature of 12°C and maximum 25°C, during the
period of the experiment.
With respect to use of inoculant only in the number of
pods in Cafeara experiment showed positive effect with
P<0.01 (Table 2), no significant response was obtained in
the experiment Florestopolis (Table 3). Souza et al.
(2011) also found that seed inoculation only increased
the number of pods per plant in the absence of N. Was
found significative F values in most parameters in the
analysis of the effect of N rates. The bean yield ranged
-1
from 1883 to 2037 kg.ha in Cafeara and 525 to 705
-1
kg.ha Florestopolis. The values obtained in Cafeara
were well above the average of 951 kg.ha-1 in Brazil and
705 kg.ha-1 in the Paraná State (Conab, 2011).
There was a decrease in nodulation on the roots of
bean, with regression adjusted a linear (P <0.05), when
increase the dose of mineral nitrogen in Cafeara (Figure
2A). In Florestopolis no significant adjustment for
nodulation between treatments with doses of N, in the
absence and presence of inoculation (Figure 2B). In
Rebeschini et al.
3159
Table 2. Mean values of nodulation, pod number, plant dry matter, leaf N and yield of beans in doses of inoculant and nitrogen. Cafear a –
PR.
o
Treatments
Nodulation (number plant-1)
Nodules weigth
(mg.plant-1)
N of pods
per plant
N foliar (g.kg-1)
Yield (kg.ha-1)
Inoculation
Yes
No
F
20.6
24.4
3.09
43.4
53.1
3.64
9.32
8.38
7.59**
37.5
36.9
0.65
1996.9
1983.8
0.073
N doses (Kg.ha-1)
0
20
40
60
80
F
CV
a
ab
33.7
ab
25.5
b
19.6
b
16.2
17.4 b
9.13**
30.6
c
57.6
a
63.2
ab
46.2
b
35.3
38.9 b
4.43**
33.1
b
7.46
bc
8.45
abc
8.91
ab
9.03
10.4 a
7.86**
8.85
34.9
ab
36.7
ab
37.0
ab
38.1
39.2 a
3.61*
6.42
1932
2001
1949
2046
2021
0.79
7.66
* and ** significant at the 5% and 1% probability according to the F test.
Table 3. Mean values of nodulation, pod number, plant dry matter, leaf N and yield of beans in doses of inoculant and nitrogen.
Florestopolis – PR.
Treatments
Inoculation
Yes
No
F
N doses(Kg.ha-1)
0
20
40
60
80
F
CV
Nodulation
-1
(number plant )
Nodules weigth
-1
(mg.plant )
No of pods
per plant
N foliar (g.Kg )
Yield (Kg.ha )
36.9
30.3
2.24
45.8
44.7
0.011
3.50
3.78
1.05
37.1
37.6
1.53
612
624
0.185
46.3
26.6
34.3
33.9
26.9
2.54
42.1
79.4 a
43.2 b
34.9 b
38.4 b
29.2 b
7.34**
46.4
3.21
3.68
3.58
4.01
3.71
0.85
24.3
37.2 ab
36.5 b
36.5 b
38.1 a
38.6 a
3.75*
3.65
509 b
642 a
658 a
656 a
624 ab
-1
-1
14.1
* and ** significant at the 5% and 1% probability according to the F test.
general, there was good nodulation of bean plants in the
absence of inoculation, in two locations measured.
Pelegrin et al. (2009) observed that nodulation in bean
plants that were not inoculated was similar to inoculated,
mainly due to population of native rhizobia in the soil.
Similarly, Hungria et al. (2003) observed no significant
differences in the number of nodules of bean plants
inoculated with rhizobia and that received N fertilization
(30 kg ha-1 at sowing + 30 kg ha-1 in coverage) when
comparing nodulation provided by the native population
soil. The inhibitory effect of N on nodulation is probably
plant mediated; however, differences in tolerance to
nitrate and ammonium have also been found between
rhizobial isolates when investigated in nodulated systems
(Nelsen, 1987).
There was significant adjustment (P <0.05) for increasing the number of pods when increased doses of N and
inoculated with Rhizobium in Cafeara (Figure 3A). In
Florestopolis the adjustment quadratic was significant (P
<0.05) for doses of N without inoculation (Figure 3B). In
the analysis of the dry mass of the shoot, there was a
significant adjustment quadratic (P <0.05) with increased
dry matter at the highest levels of mineral N in uninoculated treatments in Cafeara experiment (Figure 4). In
Florestopolis the regression analysis was significant (P
<0.01), showing maximum production at the dose of 40
kg N per hectare, when using inoculation. Responses of
bean to nitrogen fertilization have been variable in dry
3160
Afr. J. Agric. Res.
50
50
(A)
40
40
Nodule per plant
-0.125x
+ 41.94
♦ Inoculated
Inoculated y =y=
-0,125x
+ 41,94
R²R=2=0.3237
0,3237
(B)
■ Uninoculated
Uninoculated y = y=
-0,31x
+ 36,4+
-0.31x
R² = 20,8542*
36.4
R =0.8542*
30
30
20
20
Inoculated
-0.165x
♦ Inoculated
y =y=
-0,165x
+ 26,6+
2
R² =R0,8926*
=0.8926*
10
26.6
■ Uninoculated
Uninoculated y = y=
-0,191x
+ 37,9+
-0.191x
2 0,4251
R²
R= =0.4251
10
0
37.9
0
0
20
40
60
80
0
20
N doses(kg/ha)
40
60
80
N doses (Kg/ha)
Figure 2. Nodulation after inoculation of bean seeds and applied dosages of nitrogen in (A) Cafeara and (B) Florestopolis. *
significant differences (P<0.05).
Uninoculated y= -0.31x + 36.4
R2=0.8542*
14
Inoculated y= -0.165x + 26.6
R2=0.8926*
(A)
♦ Inoculated
Inoculated y = 0,0345x
+ 7,94 + 7.94
y= 0.345x
2
R² = 0,837*
R =0.837*
12
No de pods per plant
Uninoculated y= -0.191x + 37.9
R2=0.4251
14
Inoculated y= -0.125x + 41.94
2
12
10
10
8
8
6
6
■ Uninoculated
y = 0,03x
7,16
Uninoculated
y=+0.03x
2
R2 R
= 0,6194
=0.6194
4
R =0.3237
(B)
♦ Inoculated
Inoculated y = 0,0199x
+ 3,032
y= 0.0199x
2
R² = 0,7166
+ 3.032
R =0.7166
+ 7.16
4
2
2
2
2
■ Uninoculated
y = 0,0004x
- 0,0186x + 3,6657
Uninoculated
y= 0.0004x
+ 0.0186x
2R² = 0,8929*
+ 3.6657
R =0.8929*
0
0
0
20
40
60
80
0
20
N doses (Kg/ha)
40
60
80
N doses (Kg/ha)
Figure 3. Number of pods after inoculation in bean seed and applied dosages of nitrogen in(A) Cafeara (B) Florestopolis. * significant
differences (P<0.05).
Inoculated y= 0.0199x + 3.032
Inoculated y= 0.345x + 7.94
2
R =0.837*
matter production,
observed
effects (Carvalho et
Uninoculated
y=positive
0.03x + 7.16
2
al., 2001) or no significant
effects
(Soratto
et al., 2006). In
R =0.6194
Cafeara there was increase in the leaf N content, with
significant linear adjustment for both the inoculated
treatments (P<0.01) and for those without inoculation
(P<0.05) (Figure 5A). Quadratic adujstment was found to
be significant (P <0.05) for doses of mineral N without
inoculation in Florestopolis (Figure 5B).
In all the treatments the foliar N content was observed
-1
to be below critical level for the plants, 30 g de N kg , in
accord with Ambrosano et al. (1997), or 30 to 50 g de N
kg-1 (Malavolta et al., 1997). These levels are found in
2
R =0.7166
beans under conditions of fertility soil or native
communities of rhizobia with high efficiency (Soratto et
al., 2006; Farinelli et al., 2006). In conditions of poor soils
in N (with low organic matter) and low populations of
rhizobia symbiotically efficient the application of mineral
nitrogen has provided higher foliar N than those in plants
without fertilization (Carvalho et al., 2001).
Maximum values of nitrogen accumulation were found
around 100 and 30 kg of N per ha, in Cafeara and
Florestopolis respectively. In Brazil the average rates of
BNF contribution to the beans are of the order of 60 kg N
per hectare and represent 30 to 50% of total N
Rebeschini et al.
16
16
(B)
(A)
14
Shoot dry mass (g per plant)
3161
14
2
2 - 0,019x + 9,2443
■ Uninoculated
Uninoculated y =y=
0,0007x
0.0007x
– 0.019x +9.2443
2
R2 = 0,8429*
R =0.8429*
12
12
10
10
8
8
2
2 + 0,0052x + 2,8909
■ Uninoculated
Uninoculated y y=
= 2E-05x
2E-05x
+ 0.0052x + 2.8909
2
R2 = 0,3083
R =0.3083
2
2
y=0,0003x
0.0003x
– 0.0002x
+ 9.0429
♦ Inoculated
Inoculated y =
- 0,0002x
+ 9,0429
2
R2 = 0,516
R =0.516
6
6
4
4
2
2
2
2
♦ Inoculated
Inoculated y y=
= -0,0005x
+ 0,036x
+ 2,7694
-0.0005x
+ 0.036x
+ 2.7694
2 R2 = 0,9481**
R =0.9481**
0
0
0
20
40
60
0
80
20
N doses (kg/ha)
40
60
80
N doses (kg/ha)
Figure 4. Dry mass of shoots in bean seed inoculation and after applied dosages of nitrogen in (A) Cafeara and (B) Florestopolis. * and
**significant differences (P<0.05) and (P<0.01).
50
50
45
45
(A)
Leaf N content (g/kg)
40
(B)
40
35
♦Inoculated
Inoculated y y=
= 0,06x
+ 35,08
0.06x
+ 35.08
R2 R
= 20,9939**
=0.9939**
30
25
■ Uninoculated
Uninoculated y =y=
0,0395x
+ 35,3
0.0395x
+
R22=0.8247*
= 0,8247*
R
20
35.3
35
2
2
f-0.0002x
♦Inoculated
Inoculated y y=
= 0,0002x
+ 0,0149x
+ 35,946+
+ 0.0149x
2
R2 = 0,7411
R =0.7411
30
25
20
15
15
10
10
5
5
0
2
2
■Uninoculated
Uninoculated y =
- 0,0843x
+ 38,163
y=0,0012x
0.0012x
- 0.0843x
+
2
R2 = 0,927*
R =0.927*
35.946
28.163
f
0
0
20
40
60
80
N doses (kg/ha)
0
20
40
60
80
N doses (kg/ha)
Figure 5. Leaf N content in bean seed inoculation and after applied dosages of nitrogen in (A) Cafeara and (B) Florestopolis. * and
**significant differences (P<0.05) and (P<0.01).
accumulated by the plant (Pelegrin et al., 2009). Moura et
al. (2009) concluded that the species of native soil
rhizobia are as efficient as the inoculation of R. tropici in
supplying N to the bean. The responses to nitrogen
fertilization in grain production also has generated much
controversy, about that Souza et al. (2011) found that
yield of bean is little influenced by nitrogen fertilization.
Soratto et al. (2006), in studies conducted in Brazilian
Midwest, found significant responses for yield of bean,
with the estimated dose for maximum response of bean
-1
in 140 kg ha N. Farinelli et al. (2006), in Brazilian
southeast, evaluated the application of N in bean crops
under no tillage and conventional tillage and found the
maximum yield with the application of 185 kg N per
hectare. There has been variability in responses in
productivity to levels of mineral N, mainly influenced by
the levels of soil fertility and other techniques used in
production systems, especially the use of irrigation
systems (Pelegrin et al.,f 2009).
Concerning the performance of bean production in both
locations, it was observed that the experiment of Cafeara
had a good grain yield, indicating that this area is two
3162
Afr. J. Agric. Res.
years in no tillage. Soratto et al. (2006) also found
increased yield of common bean in no-tillage, with
inoculation and application of N in coverage. In
Florestopolis showed low organic matter content in the
soil (Table 1) and conventional soil management may
have contributed to reduction in crop development
especially in conditions of water stress, which may have
been decisive for the low yield of bean.
The bean yield is affected by soil water availability. A
deficiency of water in soil can reduce productivity in
different proportions according to the different phases of
the cycle, especially during flowering and early pod
formation (Fancelli and Dourado, 2007).
Under the conditions of this study it was found
adequate nodulation of bean and no response to
inoculation of bean and supply of mineral N in two
different situations, differentiated mainly by soil
management. It was observed that the condition of the
experiment Cafeara where good crop yield, factors N
dose or inoculation was found was not used in
determining this result. In this condition it can be
suggested that the N mineralization of organic matter and
FBN due to the presence of rhizobia provided greater
supply of N to the bean. A favorable environment in the
rhizosphere is vital to interaction legume-rhizobium;
however, the magnitude of the stress effects and rate of
inhibition of the symbiosis usually depend on the phase
of growth and development, as well as the severity of the
stress. For example, mild water stress reduces only the
number of nodules formed on roots of soybean, while
moderate and severe water stress reduces both the
number and size of nodules (Williams and De Mallorca,
1984). Other factors are also related to the failure of
inoculations as the competitiveness and efficiency of
strains of Rhizobium inoculant and also by the response
of bean genotype used.
Conclusions
In this study there was no response to inoculation in the
evaluation of the yield of bean in Cafeara and
Florestopolis with different soil management. The
nodulation of bean with Rhizobium is reduced by
fertilization with mineral nitrogen. The system with tillage
cultivation favored the development of bean during
autumn-winter.
Conflict of Interest
The authors have not declared any conflict of interest.
REFERENCES
Ambrosano EJ, Tanaka RT, Mascarenhas HAA, Raij BVan, Quaggio
JA, Cantarella H (1997). Leguminosas e oleaginosas. In: Raij BVan,
Cantarela H, Quaggio JA, Furlani AMC, eds. Recomendações de
adubação e calagem para o Estado de São Paulo. Campinas,
Instituto Agronômico de Campinas 187:203.
Araújo FF, Carmona FG, Tiritan CS, Creste JE (2007). Fixação
biológica de N2 no feijoeiro submetido a dosagens de inoculante e
tratamento químico na semente comparado à adubação nitrogenada.
Acta Sci. Agron. 29(4):535-540.
Binotti FFS, ARF O, Cardoso ED, Sá ME, Buzetti S, Nascimento V
(2010). Fontes e doses de nitrogênio em cobertura no feijoeiro de
inverno irrigado no sistema plantio direto. Biosci. J. Uberlândia
26(5):770-778.
Carvalho MAC, Furlani EJ, Arf O, Sá ME, Paulino H, Buzetti S (2001).
Produtividade e qualidade de sementes de feijoeiro (Phaseolus
vulgaris L.) sob influência de parcelamentos e fontes de nitrogênio.
Rev. Bras. Ciência do Solo 25(6):617-624.
Conab–Companhia Nacional de Abastecimento – prospecção safra
2011-2012. Conab feijão- Acesso em 21 mai 2012. www.conab.br
Farinelli R, Lemos LB, Penariol FG, Egéa MM, Gasparoto MG (2006).
Adubação nitrogenada de cobertura no feijoeiro, em plantio direto e
convencional. Pesquisa Agropecuária Bras. 41(3):307-312.
http://dx.doi.org/10.1590/S0100-204X2006000200016
Fancelli AL, Dourado Neto D (2007). Produção de Feijão. Piracicaba:
ESALQ/USP, Departamento de Agricultura P. 386.
Ferreira EPB, Martins LMV, Xavier GR, Rumjanek NG (2011).
Nodulação e produção de grãos em feijão-caupi (Vigna unguiculata
L. Walp.) inoculado com isolados de rizóbio. Rev. Caatinga Mossoró
24(4):27-35.
Ferreira DF (2000) Variance analysis system for balanced date. Lavras:
UFLA.
Hungria M, Campo RJ, Mendes I (2003). Benefits of inoculation of the
common bean (Phaseolus vulgaris) crop with efficient and
competitive Rhizobium tropici strains. Biol. Fertil. Soils 39(2):88-93.
http://dx.doi.org/10.1007/s00374-003-0682-6
Kabahuma MK (2013) Enhancing biological nitrogen fixation in common
bean (Phaseolus vulgaris L). Graduate Theses and Dissertations. P.
13162.
Malavolta E, Vitti GC, Oliveira AS (1997). Avaliação do estado
nutricional das plantas: princípios e aplicações. 2. ed. Piracicaba:
Potafos P. 319.
Melo SR, Zilli JE (2009). Fixação biológica de nitrogênio em cultivares
de feijão-caupi recomendadas para o Estado de Roraima. Pesquisa
agropecuária Bras. 44(9):1177-1183.
Moura JB, Guareschi RF, Correia AR, Gasolla AF (2009). Produtividade
do feijoeiro submetido à adubação nitrogenada e inoculação com
Rhizobium tropici. Global Sci. Technol, 2(3):66-71.
Muller SH, Pereira PAA (1995). Nitrogen fixation of common bean
(Phaseolus vulgaris L.) as affected by mineral nitrogen supply at
different growth stages. Plant Soil 177:55-61.
http://dx.doi.org/10.1007/BF00010337
Pelegrin R, Mercante FM, Otsubo IMN, Otsubo AA (2009). Resposta da
cultura do feijoeiro à adubação nitrogenada e à inoculação com
rizóbio. Rev. Bras. de Ciência do Solo, 33(1):219-226.
http://dx.doi.org/10.1590/S0100-06832009000100023
Nelsen LM (1987). Response of Rhizobium leguminosarum isolates to
different forms of inorganic nitrogen during nodule development in
pea (Pisum sativum L.). Soil Biol. Biochem. 19:759–763.
http://dx.doi.org/10.1016/0038-0717(87)90060-5
Romanini Júnior A, Arf O, Binotti FFS, Sá ME, Buzetti S, Fernandes FA
(2007). Avaliação da inoculação de rizóbio e adubação nitrogenada
no desenvolvimento do feijoeiro, sob sistema plantio direto. Biosci. J.
23(4):74-82.
Sant’ana EVP, Santos AB, Silveira PM (2011). The efficiency of use of
nitrogen applied in top dressing in irrigated beans. Revista Brasileira
de Engenharia Agrícola e Ambiental 15(5):458-462.
Schulze J (2004). How are nitrogen fixation rates regulated in legumes?
Plant Nutr. Soil Sci. 167:125–137.
Silveira PM, Stone LF (1979). Balanço de água na cultura do feijão em
Latossolo Vermelho Amarelo. Pesquisa Agropecuária Bras. 14:111116.
Soratto RP, Carvalho MAC, Arf O (2006). Fertilidade do solo e nutrição
de plantas. Rev. Bras. de Ciência do Solo. 30(1):259-265.
http://dx.doi.org/10.1590/S0100-06832006000200007
Rebeschini et al.
Souza EFC, Soratto RP, Pagani FA (2011). Aplicação de nitrogênio e
inoculação com rizóbio em feijoeiro cultivado após milho consorciado
com braquiaria, Pesquisa Agropecuária Bras. 46(4):370-377.
http://dx.doi.org/10.1590/S0100-204X2011000400005
Stocco P; Santos JCP, Vargas VP, Hungria M (2008). Avaliação da
biodiversidade de Rizóbios simbiontes do feijoeiro (Phaseolus
vulgaris L.) em Santa Catarina. Rev. Bras. de Ciencia do Solo.
32(3):1107-1120.
http://dx.doi.org/10.1590/S0100-06832008000300019
Williams PM, De Mallorca MS (1984) Effect of osmotically induced leaf
moisture stress on nodulation and nitrogenase activity of Glycine
max. Plant Soil 80:267–283.
http://dx.doi.org/10.1007/BF02161183
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Vol. 9(42), pp. 3164-3170, 16 October, 2014
DOI: 10.5897/AJAR2013.7302
Article Number: ADA7A6848081
ISSN 1991-637X
Copyright © 2014
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Review
Gender mainstreaming in smallholder agriculture
development: A global and African overview with
emerging issues from Swaziland
Robert Mabundza1, Cliff S. Dlamini2* and Banele Nkambule3
1
Faculty of Natural and Agriculture Sciences, Centre for Sustainable Agriculture, at the University of the Free State,
South Africa.
2
WWF South Africa, C/O Table Mountain Fund, Centre for Biodiversity Conservation, Kirstenbosch National Botanical
Gardens, PO Box 23273, Claremont, 7735, South Africa.
3
World Vision Swaziland, P. O. Box 2870 Mbabane Swaziland.
Received 29 April, 2013; Accepted 19 September, 2014
The paper presents a review of literature in gender mainstreaming in agricultural development. It begins
by defining key terms related to gender mainstreaming, and then followed by the discussion of the
historical background of gender mainstreaming. This is then followed by review of literature concerning
the gender mainstreaming and agricultural development, and African perspective of gender
mainstreaming and the current status of gender mainstreaming in Swaziland. The review ends by
discussing the potential benefits that can be harnessed from mainstreaming gender in the country’s
agriculture sector and development programmes.
Key words: Gender, mainstreaming, smallholder farming, sustainable agriculture, development.
INTRODUCTION
The concept of gender mainstreaming
Engendering
Engendering is a term that is used to refer to ways in
which gender based roles have been demystified to
ensure participation of males and females, especially in
the community development process. Engendering
development means engaging men and women equally
in the production process. This is geared towards
enhancing
equality
in
sustainable
agricultural
development. On the other hand, gendering is the
dynamic way that makes female roles adapt, established
or confirmed, clearly demonstrated in the historicity of
gender relations in both private and public spheres. Due
to the changing nature of economy and human needs,
there is a need to engender development process and
streamline the stereotypes that have characterized the
*Corresponding author. E-mail: [email protected]; [email protected]. Tel: +27 (0)21 762 8525;
Fax: 086 775 15568.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
Mabundza et al.
arena of development (Maseno and Kilonzo, 2010: 48).
Gender
Gender is defined as the set of characteristics, roles and
behavior patterns that socially and culturally distinguish
women from men in the society. Gender characteristics
change overtime and differ from one culture to another
and concept of gender refers to the roles and
characteristics of women and men into the relations of
power between them (GoS, 2010). Indeed, the term
gender is often misunderstood and used indiscriminately
as a synonym for sex. As mentioned earlier, “gender”
refers to socially constructed differences between sexes,
while sex refers to relatively fixed biological differences
between men and women (Sanga, 2008: 107).
Gender equality
Gender equality refers to equal opportunities and
outcomes for women and men in the community.
According to AfDB (2010), gender equality involves the
removal of discrimination and structural inequalities in
access to resources, opportunities and services, and the
promotion of equal rights. Equality does not mean that
women should be the same as men. However, promoting
equality recognises that men and women have different
roles and needs, and takes these into account in
development planning and programming.
Gender mainstreaming
Gender mainstreaming is defined as the process of
assessing the implications for women and men of any
planned action, including legislation, policies or
programmes, in all areas and at all levels. It is a strategy
for making women’s as well as men’s concerns and
experiences, an integral dimension of the design,
implementation, monitoring and evaluation of policies and
programmes in all political, economic and societal
spheres so that women and men benefit equally and
inequality is not perpetuated (Dayanandan, 2011: 510).
Gender mainstreaming means the process of identifying
gender gaps and making the concerns and experiences
of women, men, girls and boys integral to the design,
implementation, monitoring and evaluation of policies and
programmes in all spheres so that the benefits are
equitably distributed (GoS, 2010).
Gender and development (GAD)
GAD is an approach based on the premise that
development cooperation programs cannot succeed or
3165
the impact be sustained if the people affected do not
support them. It examines the ways in how men’s and
women’s differing roles, responsibilities, resources and
priorities may affect project implementation.
HISTORICAL
BACKGROUND
MAINSTREAMING
OF
GENDER
Around the world, gender mainstreaming has emerged as
a key gender equality strategy following the 1995 Beijing
women’s conference (Alston, 2006: 123). Rural
communities, where some 70% of the world’s rural poor
are concentrated, generally rely on agriculture, forestry,
fisheries and livestock for their livelihoods. Within those
communities, the poorest of the poor are often women
and young girls; and FAO (2010) stated that 6 out of 10
of the world’s poorest people are women who lack
regular and decent employment and income. The reports
further states that these women face hunger and/or
malnutrition, poor access to health, education and
productive
assets,
time
poverty
caused
by
disproportionate paid and unpaid work burdens and child
labour. This is in contrast to the fact that women tend to
be the main producers of food, while men manage most
of the commercial crops (FAO, 2010).
Since the mid 1980s, there has been a growing
consensus that sustainable development requires an
understanding of both women’s and men’s roles and
responsibilities within the community and their relations to
each other. This has come to be known as the GAD
approach. Improving the status of women is no longer
seen as just women’s issue, but as a goal that requires
the active participation of both men and women. For
instance in Columbia, Nilsson et al. (2009) reported that
due to the armed conflict, both women and men face
problems accessing their land. Women, however, are
discriminated against in a disproportionate manner;
although legally they are entitled to land ownership, in
practice they struggle to exercise this right.
As a consequence, when demining activities take place
and land is released, women often lose access to their
land as a result of gender-based discrimination.
Moreover, profits made from these land areas seldom
benefit women. Statistics reveal that women own less
than 5% of the world’s titled land, a critical perspective of
gender mainstreaming in African countries (Nilsson et al.,
2009).
GENDER AND AGRICULTURAL DEVELOPMENT
The section identifies gender gaps and potential benefits
that can be observed from mainstreaming gender in the
design, implementation, monitoring and evaluation of
agricultural programmes and projects to achieve
sustainable development.
3166
Afr. J. Agric. Res.
Improved socio-economic status of smallholder
farmers
Women are crucial in the translation of the products of a
vibrant agriculture sector into food and nutritional security
for their households. They are often the farmers who
cultivate food crops and produce commercial crops
alongside the men in their households as a source of
income (World Bank, 2009). Gender equality is
fundamental for achieving agricultural development
supported by economic empowerment so that more
women receive secondary and tertiary educations to
enhance their chances of finding jobs. According to World
Bank
(2009),
gender
equality
and
women’s
empowerment are far from being achieved, although
women play a significant role in agriculture.
Access and control of productive resources
Despite women prominent role in food production, market
and processing, women have limited access to land,
agricultural extension services, credit, infrastructure,
technology and markets that are crucial for enhancing
their productivity (Mthuli et al., 2011). In North Africa
32.6% of women and 32.9% of men are employed in the
agriculture sector, while the figures are much higher in
sub-Saharan Africa where 67.9% of women and 62.4% of
men are employed in the sector in 2007 (AfDB, 2010).
Studies that were conducted in Kenya have shown that
where women farmers have equal access to inputs,
education and technology their productivity increases by
20%. African agricultural and rural development efforts
cannot afford not to invest in initiatives that increase the
productivity of women engaged in agriculture (Mthuli et
al., 2011).
World Bank (2007) cited in AfDB (2010) stated that
gender inequalities in access to productive resources,
opportunities and services limit agricultural productivity
and undermine sustainable and inclusive development in
the sector. For example, in Kenya women contribute 80%
labor to food production, account for 70% of the
agriculture workers and manage 40% of the smallholder
farms and yet they receive less than 10% of the credit
allocated to small holders, hold less than 10% of the
registered land titles and receive less than 5%
agricultural credit.
Agriculture projects financed by AfDB indicate that
considerations related to gender issues and women’s
participation influence the success and sustainability of a
project. Women are major contributors to the economy,
both through their remunerative work on farms and
through the unpaid work they traditionally render at home
and in the community. Yet in many societies they are
systematically excluded from access to resources,
essential services, and decision making. The
predominance of patriarchy in law, policy, and practice
ensures that the land has owners but that they are not
women (Mbote, 2005).
Improved and meaningful participation of women in
agriculture
Women contribute tremendously to agricultural output but
unfortunately they hardly benefit from agricultural
incentives and innovation because of economic
suppression and traditional practices which undermine
the constitutional provisions on the equality of men and
women. Gender discrimination, rather than ignorance, is
the reason for the lack of women participation in
agricultural programmes and projects (Ogunlela and
Mukhtar, 2009: 21). The loss of biodiversity in agriculture
is the key threat to food security and sustainability in
relation to diversity in food, feed, fish, and animal stocks
which has narrowed down alarmingly. For example, in
rural India, it is women who conserve biodiversity on farm
as well as ex situ through various rituals. The role of
women as custodians of agriculture and livestock still
cannot be ruled out (Satyavathi et al., 2010: 44). They
depend greatly on the environment for their basic needs
such as fuel, water, food and medicine. According to a
study that was conducted in the Philippines, it was noted
that while women play important economic roles in
fishing, particularly in processing and marketing, their
roles are often neglected in programmes and projects in
the sector. Women are particularly concerned about
overfishing, which is reducing the viability of fishing
communities, and are keen to participate in protection
and sustainable management efforts (ADB, 2006). In
addition, women, whose carbon footprint is smaller than
that of men, should play a larger role in confronting
climate change since they make the majority of
consumption decisions for households (OECD, 2008).
Increased agricultural productivity
Ogunlela and Mukhtar (2009: 20) mentioned that women
are known to be more involved in agricultural activities
than men in sub-Saharan African countries, Nigeria
inclusive. As much as 73% were involved in cash crops,
arable and vegetable gardening, while postharvest
activities had 16% and agroforestry, 15%. It is estimated
that women’s contribution to the production of food crops
range from 30% in the Sudan to 80% in the Congo
contributing substantially to national agricultural
production and food security, while being primarily
responsible for the food crops. Samuel and Dasmani
(2010: 441) noted farm level efficiency can improved
particularly when tradition, culture and other socioeconomic constraints that hinder women participation are
removed. Therefore, it is critical to amplify women’s
participation in agricultural development to increase their
Mabundza et al.
economic roles in rural development particularly in food
production.
Policy development
Mbote (2005) stated that under all systems of law in
many African countries, land ownership is anchored in
patriarchy. Law can be used to reinforce or make
permanent social injustices in the realm of women’s
rights, legal rules may give rise to or exacerbate gender
inequality. Legal systems can also become obstacles
when change is required: often the de jure position, which
may provide for gender neutrality, cannot be achieved in
practice due to numerous obstacles (Mbote, 2005).
According to Mbote (2005), the predominance of
patriarchy in law, policy, and practice ensures that the
land has owners but that they are not women. For law
and policy to influence gender relations to land tenure,
there is need to deconstruct, reconstruct, and
reconceptualise customary law notions around the issues
of access, control, and ownership. The view should be to
intervene at points that make the most difference for
women. There is need for innovative and even radical
approaches. In determining tenure to land, rights should
be earned or deduced from an entity’s relationship to the
land. Rights should be anchored on use and subjected to
greater public good resident in the trusteeship over land
for posterity.
PERSPECTIVE OF GENDER MAINSTREAMING IN
AFRICAN COUNTRIES
Sanga (2008: 102) stated that during the closing decades
of the last millennium, the African continent witnessed the
emergence of a number of initiatives aimed at improving
the social, economic, and political condition of its citizens
that included a number of national, regional, and
international development plans such as the Poverty
Reduction Strategies, the New Partnership for Africa’s
Development and the Millennium Development Goals
(MDGs). In the pursuit of this agenda, it has been widely
recognized that women and men face different socioeconomic realities especially in the development of
sustainable agricultural activities.
According to Ogunlela and Mukhtar (2009: 19), the
bedrock of agriculture and agricultural development in
developing countries of sub-Saharan Africa is rural
development, without which all efforts in agricultural
development will be futile. A large majority of the farmers
operate at the subsistence, smallholder level, with
intensive agriculture being uncommon and women’s and
men’s roles and responsibilities in a society or culture are
dynamic and change overtime. Social, cultural, religious,
economic, political and legal factors and trends all have a
complex and profound influence on gender roles and
responsibilities and tend to impede sustainable
3167
agricultural development. Many of these factors can
constrain women’s participation in development activities.
Women have limited access to agricultural services and
inputs, are more likely to lack assets, and grow more
subsistence crops. Consequently, women farmers are
more likely to be asset-poor subsistence farmers. In subSaharan Africa, it has been calculated that agricultural
productivity could increase by up to 20% if women’s
access to such resources as land, seed, and fertilizer
were equal to men’s, yet women still face serious
constraints in obtaining essential support for most
productive resources, such as land, fertilizer, knowledge,
infrastructure, and market organization (World Bank,
2009).
Ogunlela and Mukhtar (2009: 28) noted that in subSaharan Africa, agriculture accounts for approximately
21% of the continent’s gross domestic product (GDP) and
women contribute 60 to 80% of the labour used to
produce food. Estimate of women’s contribution to the
production of food crops range from 30% in the Sudan to
80% in the Congo, while their proportion of the
economically active labour force in agriculture ranges
from 48% in Burkina Faso to 73% in the Congo and 80%
in the traditional sector in Sudan. On another note, the
World Bank (2009) stated that in sub-Saharan Africa,
women are largely responsible for selling and marketing
traditional crops such as maize, sorghum, cassava, and
leafy vegetables in local markets. However, in countries
where urban markets for these traditional crops are
expanding rapidly, such as Cameroon and Kenya, the
challenge is to ensure that women retain control over their
production, processing, and marketing. In Uganda the
strong demand for leafy vegetables (traditionally a
woman’s crop) in Kampala markets caused men to take
over their cultivation. Maseno and Kilonzo (2010: 48)
explain that considerable gender disparities exist in the
Kenyan labour market. Although women constitute about
50% of Kenya’s total population, they account for only
about 30% of the total formal-sector wage employment
and earn less than men, even after making adjustments
for the type of employment, occupation, and hours of
work. The scholars argue that women’s participation rates
are higher (compared to men’s) in rural areas, where they
are actively involved in subsistence activities and
agricultural production in addition to unpaid domestic
work.
This is further evidenced by Maseno and Kilonzo
(2010: 48) that they spend more than 8 h in a day
working in the fields in order to provide for their families
with basic needs. Studies have documented that women
work 12 to 13 h a week more than men, as the prevalent
economic and environmental crises have increased the
working hours of the poorest women. Women work hard
to cope with their household chores like collecting
firewood and fetching water from wells or rivers that may
be far away from home, besides other activities. Most of
these activities are recognized as ‘minor’ household jobs
that are meant for women and are hardly shared with the
3168
Afr. J. Agric. Res.
spouses or the sons. Ara (2012: 8) noted that in the
developing world, gender difference in the labour market
is a common issue and it needs to be studied in relation
to the social customs in those countries.
According to IFAD (2011), women’s empowerment
benefits not only women themselves but also their
families and communities. Moreover, farm productivity
increases when women have access to agricultural inputs
and relevant knowledge. The report further states that in
Gambia, although many development interventions
actively promote the equitable control of and access to
productive lands, in practice it has been found that the
land rights of women and other disadvantaged groups
may fare better under a local bargaining process than
where redistribution is pushed by external interventions.
Concerning the contribution of gender in economic
growth, Mthuli et al. (2011) reported that in the subSaharan Africa, women contribute 60 to 80% of the
labour used to produce food both for household
consumption and for sale. Therefore, promoting gender
equality in employment is an important cornerstone to
advance women’s economic empowerment in Africa.
35% of agricultural wage employment and about 18% to
the country output (SSA, 2012). The Government of
Swaziland has noted that the sector can make a
meaningful vehicle in the fight against high levels of
poverty and unemployment. Small-scale sugarcane
farming is now practiced by many households,
particularly those in the poverty stricken areas of the
Lowveld. SSA (2012) observed that sugar industry
presents a good opportunity for small-scale farmers to
get employment, raise incomes and move out of poverty.
This is also supported by Mnisi and Dlamini (2012: 4337)
when they observed that in India, the sugar industry is
the focal point for socio-economic development in the
rural areas by mobilizing rural resources, generating
employment and higher income, and developing transport
and communication facilities. In addition, the GoS (2005)
stated that women are the major labour force in food
production in Swaziland and also are responsible for food
preparation, household hygiene, and childcare that are
linked to household nutritional status. Therefore,
women’s rights, participation, needs, education and
training, need to be recognized in all aspects of
agricultural production.
Gender mainstreaming in Swaziland
The government of Swaziland has made some progress
in promoting gender in alignment with regional and
international commitments in providing equitable
opportunities for women and men, boys and girls at all
levels, women empowerment and social justice. This has
been achieved through the enactment and the
implementation of the nation gender policy. Through the
policy the government of Swaziland conducts capacity
building for gender mainstreaming in all national and
sectoral policies, plans, programmes and budgets. It also
focuses on strengthening the capacity and capability of
the gender unit. The unit is responsible for the
coordination of the implementation, monitoring and
evaluation of the gender policy; advocate for the
allocation of resources and public expenditure so that
they are equally beneficial to men and women;
strengthening partnerships with development partners,
Non-Governmental
Organizations
(NGOs)
and
community leaderships for gender equity and the
empowerment of women and girls. Lastly, mobilizing at
all levels for social transformation on gender issues and
for the implementation of the gender policy. The section
discusses the potential benefits that can be harnessed
from mainstreaming gender in the country’s agriculture
sector and development programmes.
Socio-economic
improvement
sugarcane farmers
of
smallholder
In Swaziland, the sugar sector is central to the economy
of Swaziland accounting for 59% of agricultural output,
Access and control of productive resources
Access and control over resources is also gendered as
only males access Swazi Nation Land (SNL) through
paying allegiance to the chief. Women on the other hand
do not access resources in their own right and may only
access land through males, as husband, father or son
and other male relatives (LUSIP, 2011). Presently,
women in Swaziland are particularly vulnerable to
poverty, with about twenty percent (20%) of households
are headed by women (GoS, 2006). Women are mainly
disadvantaged by the Swaziland law and custom which
deprives women from the right to own land and access to
finances. The customary law of the country states that
widowed women traditionally do not inherit land, but are
allowed to remain on the matrimonial land and home until
death or remarriage. Women also have less access to
education in rural areas. It is estimated that over 70% of
women in rural Swaziland are illiterate, compared to the
national average of 21% (McKnight, 2009). According to
GoS (2006), about 63% of female-headed households
are poor and lack productive assets compared to 52%
male counterparts. Therefore, agricultural production can
be used as a source of income through crop production
forestry, fisheries and animal production.
Equal and meaningful participation
The Government of Swaziland has recognized the need
to ensure equitable and full participation of women and
men at all levels of development. Deliberate efforts have
Mabundza et al.
been employed to ensure that the barriers that prevent
full and effective participation of women and men in all
sectors are removed (GoS, 2010). The government of
Swaziland developed a gender policy that aims to
address the inequities between women and men. It
provides a vision to improve the living conditions of
women and men including practical and forward looking
guidelines and strategies for the implementation,
monitoring and evaluation of the related constitutional
provisions. Ogunlela and Mukhtar (2009: 22) examined
the level of participation of rural women in the decisionmaking in different areas of agriculture and studied
factors influencing their participation in the decisionmaking process in farm management. They found that
women’s participation in decision making was quite
minimal. In each of the farm operations, less than 20% of
the women were consulted, except in the sourcing of
farm credit, where about 28% were consulted; about 13%
or less of the women had their opinion considered in
each of the farm operations. However, only between 1.0
and 2.5% took the final decision in all of the farm
operations.
The role that women play and their position in meeting
the challenges of agricultural production and
development are quite dominant and prominent. Findings
from a study financed by the United Nations
Development Programme (UNDP) revealed that women
make up some 60 to 80% of agricultural labour force in
Nigeria (Ogunlela and Mukhtar, 2009: 20), depending on
the region and they produce two-thirds of the food crops.
In contrast, widespread assumption that men and not
women make the key farm management decisions has
prevailed.
3169
note, the Government of Swaziland has undertaken
numerous initiatives to ensure full and coherent
participation of women and men in achieving the national
objectives of economic growth, self-reliance, social
justice and stability through involving women and men in
the mainstream of the country’s development (GoS
2010).
Conclusion
The research reviewed gave understanding of gender
mainstreaming in agricultural development with reference
to small-scale farmers involved in crop production. It also
highlighted the critical issues that hinder participation of
women in agriculture in African countries including
Swaziland with regards to access and control of
productive resources, participation of men and women in
agricultural development and the potential benefits of
gender mainstreaming in policy development and its
positive contribution into the improvement socioeconomic status of farmers and poverty reduction.
Furthermore, it highlights the progress of the government
of Swaziland in creating a suitable environment for
implementing gender mainstreaming in community
development activities.
Conflict of Interests
The author(s) have not declared any conflict of interests.
ACKNOWLEDGEMENTS
Policy development
In most African countries female farmers are among the
voiceless, especially with respect to influencing
agricultural policies (Ogunlela and Mukhtar, 2009: 19).
Policies which are aimed at increasing food security and
food production tend to either underestimate or totally
ignore women’s role in both production and the general
decision-making process within the household.
The promotion of gender equity and the empowerment
of women have been recognized as a key millennium
development goal and to meet this goal, the Kingdom of
Swaziland has developed a gender policy to provide
guidelines for attaining gender equity in the country. The
development of appropriate policies and strengthening of
national gender machineries to fully undertake the
challenging mandates are crucial actions particularly in
addressing structural relationships of inequality between
men and women. The national gender policy provides
guidelines, indicators and a framework to assist
stakeholders to achieve gender equity as provided for in
the country and other relevant international instruments
that the country has ratified (GoS, 2010). On another
The University of the Free State, WWF South Africa and
World Vision Swaziland are highly appreciated for their
invaluable support throughout the preparation of this
review paper. Special thanks go to all authors cited in the
text.
REFERENCES
ADB (2006). Gender checklist: Agriculture. Asian Development Bank.
Wiley-blackwell Publishing Asia, Philippines.
AfDB (2010). Agriculture sector strategy 2010-2014. African
Development Bank, Blackwell Publishing Ltd, Oxford.
Alston M (2006). Gender mainstreaming in practice: A view from rural
Australia. NWSA 18(2):123-147.
http://dx.doi.org/10.2979/NWS.2006.18.2.123
Ara F (2012). Examining gender difference in socio-economic
development: Implications for developing countries. J. Econ. Sustain.
Dev. 3(4):8-13.
Dayanandan R (2011). Gender mainstreaming through extension:
Problems and prospects. J. Altern. Perspect. Soc. Sci. 3(3):508-550.
FAO (2010). Gender dimensions of agricultural and rural employment:
Differentiated pathways out of poverty (Online). Food and Agriculture
Organization.
Retrieved
from:
www.fao.org/docrep/013/i1638e/i1638e.pdf, (Accessed 26 February
2012).
GoS (2005). National food security policy for Swaziland. Ministry of
3170
Afr. J. Agric. Res.
Agriculture and Co-operatives, Government of Swaziland, Mbabane,
Swaziland.
GoS (2006). A poverty reduction strategy and action plan. Ministry of
Economic Planning and Development, Government of Swaziland,
Mbabane, Swaziland.
GoS (2010). National gender policy. The Deputy Prime Minister’s Office,
Government of Swaziland, Mbabane, Swaziland.
IFAD (2011). Gender (online). International Fund for Agricultural
Development. Retrieved from: www.ifad.org/gender/index.html.
Accessed 7 March 2011.
LUSIP (2011). Gender policy. Lower Usuthu Smallholder Irrigation
Project, Swaziland Water and Agricultural Development Enterprise,
Mbabane, Swaziland.
Maseno L, Kilonzo SM (2010). Engendering development: Demystifying
patriarchy and its effects on women in rural Kenya. Int. J. Sociol.
Anthropol. 3(2):45-55.
Mbote PK (2005). Gender issues in land tenure under customary law.
Consultative Group on International Agricultural Research (CGIAR),
Washington.
McKnight M (2009). Overcoming food insecurity in Swaziland. Carmel,
Indiana,
United
States
of
America.
Retrieved
from:
www.worldfoodprize.org/CarmelHS_MMcKneight. Accessed: 7 March
2011.
Mnisi MS, Dlamini CS (2012). The concept of sustainable sugarcane
production: Global, African and South African perceptions. Afr. J.
Agric. Res. 7(31):4337-4343.
Mthuli N, Leyeka L, Vencatachellum D (2011). Gender in employment.
Case study of Mali (Online). African Development Bank. Retrieved
from: www.afdb.org. Accessed 16 April 2012.
Nilsson M, Rozes V, Garcia J (2009). Gender and land release: The
responsibility of mine-action community. J. ERW Min. Action
1313(1):2.
OECD (2008). Gender and sustainable development: Maximizing the
economic, social and environmental role of women. Organization for
Economic Cooperation and Development. OECD Publishing, Paris.
Ogunlela Y, Mukhtar A (2009). Gender issues in agriculture and rural
development in Nigeria: The Role of Women. Hum. Soc. Sci. 4(1):1930.
Samuel SKD, Dasmani I (2010). Gender difference and farm level
efficiency: Metafrontier production function approach. J. Dev. Agric.
Econ. 2(12):441-451.
Sanga D (2008). Addressing gender issues through the production and
use of gender-sensitive information. Afr. Stat. J. 7:101-124.
Satyavathi CT, Bharadwaj CH, Brahmanand PS (2010). Role of farm
women in agriculture: Lessons learned. Gend. Technol. Dev.
14(3):441-449. http://dx.doi.org/10.1177/097185241001400308
SSA (2012). Importance of the Sugar Sector to Swaziland’s Economy.
Swaziland Sugar Association,Mbabane, Swaziland.
World Bank (2009). Gender in agriculture. Source book. The World
Bank, Washington.
Vol. 9(42), pp. 3171-3184, 16 October, 2014
DOI: 10.5897/AJAR2013.7722
Article Number: 38CA0E548088
ISSN 1991-637X
Copyright © 2014
Author(s) retain the copyright of this article
http://www.academicjournals.org/AJAR
African Journal of Agricultural
Research
Full Length Research Paper
Effect of tillage system and nitrogen fertilization on
organic matter content of Nitisols in Western Ethiopia
D. Tolessa1, C. C. Du Preez * and G. M. Ceronio2
1
Ethiopian Institute of Agricultural Research, P. O. Box 2003, Addis Ababa, Ethiopia.
Department of Soil, Crop and Climate Sciences, University of the Free State, P. O. Box 339, Bloemfontein
9300, South Africa.
2
Received 30 July, 2013; Accepted 10 September, 2014
Ethiopian soils, formed from old weathered rocks, are naturally low in fertility. Moreover, crop
production is constrained by non-sustainable cropping practices, particularly repeated plowing and
hoeing, which enhances loss of soil organic matter. Field trials were therefore conducted to determine
the integrated effects of tillage system and nitrogen fertilization on organic matter content of Nitisols at
five sites using maize as test crop during 2000 to 2004 in Western Ethiopia. Three tillage systems
[minimum tillage with residue retention (MTRR), minimum tillage with residue removal (MTRV) and
conventional tillage (CT)] and three N levels (the recommended rate and 25% less and 25% more than
this rate) were combined in factorial arrangement with three replications. After 5 years, the two
measured indices of organic matter, viz. the organic carbon (C) and total nitrogen (N) content were both
significantly higher in the MTRR soil compared to the CT and MTRV soils. However, the influence of
tillage systems on organic C and total N was confined to the upper 0 to 7.5 cm soil layer. On average,
organic C and total N in this layer were 17 and 25%, and 20 and 29% higher with MTRR than with CT and
MTRV, respectively. Application of N fertilization for 5 consecutive years showed profound effects on
organic C and total N. Increasing levels of N application led to higher organic C and total N irrespective
of tillage system. Both organic C and total N showed however a steady (115 kg ha -1 N level) to declining
-1
(69 and 92 kg ha N levels) trend over time. Based on these results, replacement of CT with MTRR
should be beneficial and sustainable to soil quality in the study area. However, MTRV is not an option at
all to replace CT from a soil quality point of view. This study’s findings may be applicable to other
highland regions in Africa where cropping on Nitisols is common.
Key words: Conventional tillage (CT), crop residues, minimum tillage, organic carbon, total nitrogen.
INTRODUCTION
Western Ethiopia has a high potential for maize
production due to favourable environmental conditions.
This potential is seldom realized if at all because of non-
sustainable cropping systems. Practices often mentioned
contributing to the phenomenon are plough-based tillage
resulting in soil and water loss through erosion
*Corresponding author. E-mail: [email protected]. Tel: +27-51-4012212; Fax: +27-51-4012212.
Author(s) agree that this article remain permanently open access under the terms of the Creative Commons Attribution
License 4.0 International License
3172
Afr. J. Agric. Res.
(Bezuayehu et al., 2002) and insufficient fertilization
resulting in poor growth and development of crops
(Tolessa et al., 2002). Farmers will probably proceed with
these practices for many years to come because of their
financial limitations. Investigations into cropping systems
comprising of alternative tillage practices are therefore
justified.
Tillage plays an important role in the dynamic
processes governing soil fertility (Triplett and Van Doren,
1977; Phillips et al., 1980; Mahboubi et al., 1993; Duiker
and Beegle, 2006; Govaerts et al., 2009). Properly
designed tillage practices usually alleviate soil-related
constraints in achieving potential productivity and utility.
However, improperly designed tillage practices can set in
motion a wide range of degradative soil processes like
organic matter depletion, aggregate deterioration,
accelerated erosion, and disruption of water, carbon,
nitrogen and other major nutrient cycles (Baeumer and
Bakermans, 1973; Phillips et al., 1980; Lal, 1993; Bolliger
et al., 2006; Du Preez et al., 2011).
Several studies indicated that minimum tillage
increased organic matter on or near the soil surface
mainly due to crop residues that are not mechanically
mixed into the soil and hence decompose slower
compared to conventional tillage (CT) (Lal, 1976; Barber,
1979; Blevins et al., 1983; White, 1990; Rasmussen and
Collins, 1991; Unger, 1991; Ismail et al., 1994; Loke et
al., 2012). This increase of organic matter has on account
of its nature long-term beneficial effects on a number of
soil properties and processes. In the broadest context,
organic matter may be referred to as the total
complement of organic substances in soil, including living
organisms of different sizes, organic residues in various
stages of decomposition and dark-coloured humus
consisting of non-humic and humic substances
(Stevenson and Cole, 1999; Powlsen et al., 2013).
Claims are therefore that organic matter is a major
source of nutrients and microbial energy, holds water and
nutrients in available forms, promotes soil aggregation
and root development, and improves water infiltration and
water-use efficiency (Allison, 1973; Brady and Weil,
2008). Cropping usually benefits from the mentioned
properties and processes influenced by organic matter.
Another benefit often attributed to minimum tillage is
the sequestration of carbon (C) in the soil through higher
organic matter contents (Kern and Johnson, 1993). This
sequestrated C results in less CO2 in the atmosphere and
therefore reducing the rate of global warming. CT in
comparison with minimum tillage releases more CO 2 to
the atmosphere on account of enhanced biological
oxidation of soil organic matter (Reicosky et al., 1995;
Govaerts et al., 2009). However, Baker et al. (2007)
cautioned against the widespread belief that minimum
tillage favours C sequestration since it may simply be an
artifact of sampling depth. In line with this view, Chaplot
et al. (2012) mentioned that the impact of minimum tillage
on CO2 emissions is still a matter of debate because
research results are often not complementary.
Nevertheless, minimum tillage is widely recognized for
its role in conservation of the natural resources soil, water
and air. Minimum tillage however often necessitates
higher nitrogen (N) fertilization to maintain crop yields
during its initiation phase until a gradual build-up of the
organic N pool could compensate for sustainable
production (Larson et al., 1972; Phillips et al., 1980; Rice
et al., 1986; Smith and Elliot, 1990; Bakht et al., 2009).
This additional N will be unaffordable for most Ethiopian
farmers if not subsidized in one way or another.
Experiments were therefore conducted on Nitisols in
Western Ethiopia to examine the integrated effects of
tillage system and N fertilization on the performance of
the maize crop and the change in soil properties.
The ultimate aim with this research was to obtain
substantiated information on whether minimum tillage can
be introduced successfully into the current cropping
systems practiced in the region. Some of the results are
already reported, viz. the effects on yield and yield
components (Tolessa et al., 2007) and efficacy of applied
nitrogen (Tolessa et al., 2009). In this paper, the
response of soil organic matter is presented.
MATERIALS AND METHODS
Experimental sites
The field trials for this study were conducted under rainfed
conditions at Bako Agricultural Research Center, and on farmers’
fields at Shoboka, Tibe, Ijaji and Gudar. These five sites were
selected to be representative of the major maize producing areas of
Western Ethiopia in terms of climate and soil. Bako is located at 09°
01’N and 37° 02’E, Shoboka at 09°06’ N and 37°21’E, Tibe at
09°29’N and 37°32’E, Ijaji at 09°43’N and 37°47’E, and Gudar at
08°09’N and 38°08’E latitude and longitude, respectively.
The altitudes for Bako, Shoboka, Tibe, Ijaji and Gudar are 1650,
1695, 1730, 1820 and 2000 m above sea level, respectively. Only
climatic data of the Bako site with the lowest altitude and the Gudar
site with the highest altitude as obtained from nearby weather
stations is given in Table 1 since there are no weather stations
close to the other three sites. Based on these data the mean annual
rainfall over a 15-year period (1990 to 2004) ranged from 1042.2
mm at the higher lying Gudar site to 1239.6 mm at the lower lying
Bako site, viz. a difference of 197.4 mm. For the cropping season
(May to October) the average minimum temperature was 3.5°C
lower and maximum temperature 0.9°C higher at the Bako site
compared to that of the Gudar site.
At all five sites the soil was classified as a Nitisol (FAO, 1998).
Some physical and chemical topsoil characteristics of these Nitisols
before commencement of the trials are summarized in Table 2. The
textural class of the Nitisols differed from loam at the Ijaji site to clay
at the Shoboka site. Similar differences of 0.61 units in pH, 1.08%
in organic C, 0.04% in total N, 3.9 mg kg-1 in extractable P and 85
mg kg-1 in exchangeable K were recorded between the five sites.
The aforementioned differences in climate and soil justified
therefore the selection of the five sites for this investigation.
Field trial layout
At each of the sites a field trial was laid out in a randomized
Tolessa et al.
3173
Table 1. Climatic data for the Bako and Gudar sites as obtained from nearby weather stations.
Bako
May
June
July
1990 - 1999
2000
2001
2002
2003
2004
2000 - 2004
146.1
135.1
161.3
68.3
5.7
14.1
76.9
214.1
278.2
219.3
236.0
265.1
268.6
253.4
254.1
236.9
328.9
239.2
420.6
225.5
290.2
August
September
Rainfall (mm)
231.7
141.4
289.6
162.0
264.3
96.7
205.9
42.1
434.4
39.9
257.8
85.2
290.4
85.2
Minimum
Maximum
Mean
15.0
28.6
21.8
14.7
25.9
20.3
Temperature (°C)
14.6
14.5
14.0
23.9
24.1
25.1
19.3
19.3
19.6
Gudar
May
June
July
1990 - 1999
2000
2001
2002
2003
2004
2000 - 2004
111.4
109.9
194.0
29.5
2.0
37.4
74.6
150.4
123.8
166.6
216.2
185.9
110.0
160.5
258.5
207.5
301.5
211.6
167.3
293.5
236.3
Minimum
Maximum
Mean
11.6
28.4
20.0
11.1
25.3
18.2
11.1
23.1
17.1
August
September
Rainfall (mm)
163.3
100.7
237.5
166.6
209.7
61.0
131.0
30.2
153.2
55.9
172.1
147
180.7
92.1
October
*CS
Annual
70.8
103.4
92.7
0.0
11.5
43.5
50.2
1058.2
1205.2
1163.2
791.5
1177.2
894.7
1046.3
1243.7
1345.5
1354.2
1040.9
1355.1
1061.3
1231.4
October
*CS
Annual
74.6
19.4
17.8
17.8
7.5
28.9
18.3
858.9
864.7
950.6
636.3
571.8
788.9
762.5
1069.0
994.4
1139.4
881.8
975.9
951.5
988.6
12.6
27.2
19.9
Temperature (°C)
11.2
10.3
22.6
24.5
16.9
17.4
9.6
25.7
17.7
*CS = Cropping season.
Table 2. Some physical and chemical topsoil characteristics of the Nitisols at the study sites before
commencement of the trials.
Sites
Bako
Shoboka
Tibe
Ijaji
Gudar
Sand
Silt
Clay
------------- % --------------35.1
31.6
33.3
34.7
23.3
42.0
26.7
35.2
38.1
44.7
32.3
23.0
18.8
42.5
38.7
pH (H2O)
5.59
5.52
5.41
5.69
6.02
complete block design. The layout consisted of two factors namely,
three tillage systems: minimum tillage with residue retention
(MTRR), minimum tillage with residue removal (MTRV) and CT and
three N fertilization levels (69, 92 and 115 kg ha-1) replicated three
times in a complete factorial combination. Every field trial had
therefore 27 plots. An application of 92 kg N ha-1 is the
recommended fertilization rate for conventional maize production at
the study sites, implicating the two other rates are 25% less and
25% more than this recommended rate. These experiments were
Organic C
Total N
----------------%-----------1.77
0.15
1.65
0.14
1.46
0.12
1.93
0.16
1.69
0.14
P
K
------ mg kg-1------12.6
192
11.5
155
8.7
146
10.3
231
9.6
159
conducted from 2000 until 2004. The experimental plots were kept
permanent to observe the carry-over effects of the treatments for
the five cropping seasons.
Agronomic practices
Before the initiation of the trials the fields at all sites had been under
conventional maize production for many years. During the
3174
Afr. J. Agric. Res.
entire trial period immediately after harvesting, the plants were cut
at ground level and uniformly spread on the CT and MTRR plots,
and removed from the MTRV plots. For the MTRR and MTRV
treatments, soil disturbance was restricted to the absolute
minimum, viz. the soil was disturbed only to place the seed in the
soil at the time of sowing. In contrast, the soil was ploughed three
times with the local oxen-plough ‘maresha’ prior to sowing to obtain
a suitable seedbed for the CT treatments.
Urea and triple super phosphate were used as the sources of N
and P, respectively. The application of urea was split, and therefore
half of the urea and all of the triple super phosphate were band
placed 5 cm below the seed at sowing. At 35 days after sowing
when maize was at knee-height the other half of the urea was band
placed next to the row at 5 cm depth. The fertilizer in the small
furrows was covered with soil soon after application. All treatments
received the recommended phosphorus rate of 20 kg ha -1 annually.
Weed control in the MTRR and MTRV treatments was done by
applying Round-up (glyphosate-isopropylamine 360 g a.i. L-1) at a
rate of 3 L ha-1 prior to planting and Lasso/Atrazine
(alachlor/atrazine 336/144 g a.i. L-1) at a rate of 5 L ha-1 as a preemergence application. The recommended weed control practice
for CT in Ethiopia is hand weeding at 30 and 55 days after sowing
followed by slashing at milk stage.
The standard cultural practices as commonly recommended to
the farmers were adopted for the study. Therefore, from 2000 to
2004 the planting dates varied from 5 May to 5 June at all the sites.
A late maturing commercial maize hybrid, BH-660 was planted. The
plant density aimed for was 50 000 plants ha-1 as the 5.0 × 4.8 m
plots consisted of six rows, 5.0 m in length and the inter- and intrarow spacing was 0.8 and 0.25 m, respectively.
Data collection
Soil samples were collected, just before the trials commenced, from
the 0 to 30 cm layer of all five sites for their characterization. A 5 cm
auger was used to sample 20 randomly selected spots per site.
These subsamples were thoroughly mixed, dried at room
temperature, sieved through a 2 mm screen and stored until
analysis.
Since the trials started soil samples were collected annually after
harvesting from the 0 to 30 cm layer of all plots at each site. At the
end of the trial period, the 0 to 7.5 cm, 7.5 to 15 cm, 15 to 22.5 cm
and 22.5 to 30 cm layers were sampled additionally. In both
instances an auger with a 2 cm diameter was used to sample five
randomly selected spots per plot. These sub-samples were
prepared for analysis as described earlier.
Standard procedures (The Non-affiliated Soil Analysis Work
Committee, 1990) were used to determine particle size distribution
(Hydrometer), organic C (Walkley-Black), total N (Kjeldahl),
extractable P (Bray 2) and exchangeable K (NH4OAc) of the
relevant composite soil samples.
Statistical analysis
Experimental data were analyzed through analyses of variance
using the MSTATC statistical package (Michigan State University,
1989). Means for each parameter were compared by the least
significant difference (LSD) test at P = 0.05.
RESULTS AND DISCUSSION
Organic carbon
The effect of tillage system on organic C in the 0 to 30 cm
soil layer is displayed in Figure 1 for the year 2000 to
2004. Clear differences in the organic C development on
account of the three tillage systems as the experiments
progressed from 2000 to 2004 were observed. This
phenomenon is attributed to the fact that organic C
increased with MTRR and decreased with MTRV. In the
case of CT the organic C at Tibe and Gudar remained
almost constant, and at Bako, Shoboka and Ijaji it
declined but to a lesser degree as compared to MTRV.
The change of organic C in the 0 to 30 cm layer
resulting from the application of N fertilization at different
levels is illustrated in Figure 2. Organic C increased with
higher levels of N application though not significant in
many instances. These differences in organic C become
more apparent as the experimental period progressed
from 2000 to 2004. Over this period it appears that there
is a decreasing trend in organic C, especially with the
lowest and intermediate rates of N application. This is a
cause of concern for the long-term organic matter
content.
The organic C differences evolved in the upper 30 cm
of the Nitisols from tillage system and N fertilization had
their origin mainly in the upper 0 to 7.5 cm layer as
shown in Figures 3 and 4. In general the highest organic
C in this layer was recorded with MTRR, followed by CT
and then MTRV. Organic C also increased significantly
with higher levels of N application at three of the five
sites, viz. Bako, Ijaji and Gudar.
The application of the particular three tillage systems
on the Nitisols for 5 consecutive years caused
tremendous changes of organic C in the upper 7.5 cm
layer. Organic C in this layer was on average for all sites
with MTRR 17 and 25% more than with CT and MTRV,
respectively. This finding is consistent with results
reported by several other researchers (Baeumer and
Bakermans, 1973; Hamblin and Tennant, 1979; Griffith et
al., 1986; Mahboubi et al., 1993; Franzluebbers, 2004;
Baker et al., 2007; Luo et al., 2010; Loke et al., 2012).
They attributed the difference in organic C between
MTRR and CT to the fact that crop residues and the
organic matter originated from it are oxidized faster in CT
than MTRR soils due to a higher microbial activity. The
significance of retaining crop residues was emphasized
by the difference of organic C between the MTRR and
MTRV soils. Sufficient crop residues for retention to
ensure organic C maintenance or increase can only be
realized with proper N fertilization as was the case in this
study.
Total nitrogen
As could be expected the effect of tillage system on total
N in the 0 to 30 cm soil layer (Figure 5) was almost
similar to that of organic C (Figure 1). The total N
increased with MTRR and decreased with MTRV
resulting in large differences after 5 years. In the case of
CT, the total N also decreased but to a lesser degree
Tolessa et al.
Shoboka
2.0
2.0
1.8
1.8
1.4
1.2
Organic C (%)
Organic C (%)
Bako
1.6
1.6
LSD(0.05) Y x T = 0.15
MTRR
MTRV
CT
1.4
1.2
1.0
LSD(0.05) Y x T = 0.13
1.0
2000 2001 2002 2003 2004
Years
2000 2001 2002 2003 2004
Years
Ijaji
Tibe
2.0
2.3
LSD(0.05) Y x T = 0.12
2.1
Organic C (%)
Organic C (%)
1.8
3175
1.6
1.9
1.4
1.7
1.2
1.5
1.0
LSD(0.05) Y x T = 0.14
1.3
2000 2001 2002 2003 2004
Years
2000 2001 2002 2003 2004
Years
Gudar MTRR = Minimum tillage with residue retained
Organic C (%)
2.0
MTRV = Minimum tillage with residue removed
1.8
CT
1.6
= Conventional tillage
1.4
LSD(0.05) Y x T = 0.13
1.2
1.0
2000 2001 2002 2003 2004
Years
Figure 1. Effect of tillage system on organic C measured after harvesting in the 0-30 cm layer of Nitisols at the five
experimental sites in 2000 to 2004. Y = year and T = tillage system.
than with MTRV. The change of total N in the 0 to 30 cm
soil layer resulted from the application of N fertilizer at
different rates is shown in Figure 6. Total N increased
with higher rates of N application though not always
significant. It seems however when the recommended
rate of N or less is applied that total N like organic C
Afr. J. Agric. Res.
Bako
LSD(0.05)
ns
69
1.9
ns
1.8
1.7
1.6
Organic C (%)
Organic C (%)
1.9
1.8
-1
N levels (kg ha ):
ns
1.8
LSD
92 (0.05)
115
ns
0.12
ns 0.13
1.7
N levels (kg ha-1):
LSD
(0.05)
69
115
1.7 92
0.12
ns
ns 0.13
ns
1.6
Organic C (%)
2.0
2.0
1.7
Tibe
2.1
1.6
0.13
1.5
0.12 0.12
ns 0.12
1.4
1.4
Organic C (%)
LSD(0.05)
Organic C (%)
Organic C (%)
0.12 0.12
1.5
2.0
0.13
1.9
LSD(0.05)
ns
0.13
ns 0.12
2.0
LSD(0.05)
ns
0.13
ns 0.11
1.9
0.15
ns 0.11
0.
1.8
1.8
Gudar
1.8
LSD(0.05)
ns
ns
Organic C (%)
Organic C (%)
ns
1.7
1.7
2000 2004
2001 2002 2003 20
2000 2004
2001 2002 2003
2000 2004
2001 2002 2003
2000 2001 2002 2003
Years
Years
Years
Years
LSD(0.05)
1.4
ns
1.3
1.8
1.5
ns
ns
Ijaji
2.1
Gudar
1.6
ns
ns
1.4
1.4
2000 2004
2001 2002 2003
2000 2004
2001 2002 2003 20
2000 2001 2002 2003
2000 2004
2001 2002 2003
Years
Years
Years
Years
LSD(0.05)
1.7
ns
1.6
1.6
1.7
1.3
LSD(0.05)
1.7
1.5
1.5
Tibe
1.6
Shob
Shoboka
1.8
Organic C (%)
Bako
Organic C (%)
3176
1.7
ns
nsns
ns
ns
ns
ns
ns
1.6
1.5
1.4
2000 2004
2001 2002 2003 2004
2000 2001 2002 2003
Years
Years
Figure 2. Effect of nitrogen fertilization on organic C measured after harvesting in the 0-30 cm layer of Nitisols at
the five experimental sites in 2000 to 2004.
shows a declining trend from 2000 to 2004. Thus,
maintenance of organic matter content could be in
question over the long-term.
Inspection of Figures 7 and 8 show that the differences of
total N in the 0 to 30 cm layer which resulted from tillage
system and N fertilization are mainly attributable to
Tolessa et al.
Organic C (%)
1.5
2.0
2.5
0.0
Soil depth (cm)
LSD(0.05)
0.0
0.21
0.17
MTRR
MTRV
CT
ns
22.5
7.5
ns
15.0
ns
22.5
ns
ns
30.0
30.0
0.0
Organic C (%)
0.5
1.0
1.5
Tibe
2.0
1.0
0.0
ns
15.0
ns
22.5
ns
Soil depth (cm)
Soil depth (cm)
7.5
7.5
LSD(0.05)
Ijaji
3.0
0.19
0.17
15.0
ns
22.5
ns
30.0
30.0
0.5
Organic C (%)
Gudar
1.0
1.5
2.0
2.5
0.0
Soil depth (cm)
Organic C (%)
1.5
2.0
2.5
0.0
0.18
LSD(0.05)
LSD(0.05)
7.5
15.0
0.20
LSD(0.05)
7.5
15.0
Organic C (%) Shoboka
1.0
1.5
2.0
2.5
0.5
Soil depth (cm)
1.0
Bako
3.0
3177
0.16
ns
ns
MTRR = Minimum tillage with residue retained
MTRV = Minimum tillage with residue removed
CT
= Conventional tillage
22.5
ns
30.0
Figure 3. Effect of tillage system on organic C measured after harvesting in the 0-7.5 cm, 7.5-15 cm, 15-22.5 cm
and 22.5-30 cm layers of Nitisols at the five experimental sites in 2004 .
changes in the 0 to 7.5 cm layer. At all sites the lowest
total N recorded in this layer was observed in MTRV,
followed by CT and then MTRR. Total N increased also
with higher rates of N application though only significant
at Bako and Ijaji.
After 5 consecutive years of application, the three
3178
Afr. J. Agric. Res.
Soil dpeth (cm)
Soil dpeth (cm)
Soil depth (cm)
Soil depth (cm)
Organic C (%)
Bako
Organic C (%) Sho
Organic C (%)
Bako
Organic C (%) Shoboka
2.5 1.0
3.0 1.5 0.5
2.0
1.0
1.5
2.0 1.0
2.5 1.5
3.0 2.0 0.5
2.0 1.0
2.5 1.5
0.0
0.0
0.0
0.0
ns
ns
0.21
LSD
LSD(0.05)
LSD(0.05) 0.21
(0.05)
LSD(0.05)
7.5
7.5
7.5
7.5
ns
ns
ns
ns
15.0(kg ha-1):
15.0
15.0
15.0 (kg ha-1):
N levels
N levels
ns
ns
ns
ns
69
69
22.5
22.5
22.5
22.592
92
ns
ns
ns
ns
115
115
30.0
30.0
30.0
30.0
Soil depth (cm)
Soil depth (cm)
Soil depth (cm)
Soil depth (cm)
Organic C (%)
Tibe
Organic C (%)
Organic C (%)
Tibe
Organic C (%)
Ijaji
2.0 1.5
2.5 2.0 1.0
0.5
1.0
1.5 0.5
2.0 1.0
2.5 1.5 1.0
2.5
2.5 1.5
3.0 2.0
0.0
0.0
0.0
0.0
ns
0.19
ns
0.19
LSD
LSD(0.05)
LSD(0.05)
LSD(0.05)
(0.05)
7.5
7.5
7.5
7.5
ns
ns
0.17
0.17
15.0
15.0
ns
15.0
ns
15.0
ns
ns
22.5
22.5
ns
ns
22.5
22.5
ns
ns
30.0
30.0
30.0
30.0
Organic C (%)
Gudar
Organic C (%)
Gudar
2.0
2.5
1.0
1.5 0.5
2.0 1.0
2.5 1.5
0.0
0.16
0.16
LSD(0.05)
LSD(0.05)
7.5
ns
ns
15.0
ns
ns
22.5
ns
ns
30.0
Soil depth (cm)
0.0
7.5
15.0
22.5
30.0
Soil depth (cm)
0.5
Figure 4. Effect of nitrogen fertilization on organic C measured after harvesting in the 0-7.5 cm, 7.5-15 cm, 15-22.5
cm and 22.5-30 cm layers of Nitisols at the five experimental sites in 2004.
tillage systems resulted in large changes of total N in the
upper 7.5 cm layer of the Nitisols. The average total N in
this layer for all sites was 20 and 29% higher with MTRR
than with CT and MTRV, respectively. Similar results
were reported by various other researchers (Tripplet and
Van Doren, 1969; Phillips and Young, 1973; Lal, 1976;
Blevins et al., 1983; White, 1990; Bakht et al., 2009). The
fate of total N was therefore almost similar to that of
organic C for the same reasons given earlier. This
phenomenon is supported by the C/N ratios (Data not
Tolessa et al.
Bako
Shoboka
2.0
LSD(0.05) Y x T = 0.13
Total N (g kg-1)
Total N (g kg-1)
2.0
1.8
1.6
1.4
1.2
1.0
MTRR
MTRV
CT
1.8
LSD(0.05) Y x T = 0.14
1.6
1.4
1.2
1.0
2000 2001 2002 2003 2004
Years
2000 2001 2002 2003 2004
Years
Ijaji
1.5
2.0
1.3
1.8
Total N (g kg-1)
Total N (g kg-1)
Tibe
1.1
0.9
3179
LSD(0.05) Y x T = 0.16
0.7
0.5
1.6
1.4
1.2
LSD(0.05) Y x T = 0.15
1.0
2000 2001 2002 2003 2004
Years
2000 2001 2002 2003 2004
Years
Gudar
2.0
Total N (g kg-1)
1.8
LSD(0.05) Y x T = 0.14
MTRR = Minimum tillage with residue retained
1.6
MTRV = Minimum tillage with residue removed
1.4
CT
= Conventional tillage
1.2
1.0
2000 2001 2002 2003 2004
Years
Figure 5. Effect of tillage system on total N measured after harvesting in the 0-30 cm layer of Nitisols at the five
experimental sites in 2000 to 2004. Y = year and T = tillage system.
shown) that indicated no significant differences between
the treatments. Thus, either organic C or total N can be
used as an index to monitor the change of organic matter
content in the Nitisols.
The replacement of CT with MTRR could be considered
therefore to increase the organic matter content of
degraded Nitisols in Western Ethiopia on the condition
that the change in tillage system coincide with at least the
Afr. J. Agric. Res.
Bako
1.6
1.5
1.4
Total N (g kg-1)
1.7
1.8
1.6
N levels (kg ha-1):
N levels (kg ha-1):
LSD(0.05)
LSD(0.05)
691.5 92
115
691.7 92
115
ns
ns
0.12
0.12
ns1.4 ns
ns1.6 ns
ns
ns
Total N (g kg-1)
Total N (g kg-1)
1.8
1.2
1.1
1.0
1.3
0.14
0.15
0.16 0.15
ns
1.7
ns
1.2
1.1
0.13
ns
1.5
1.4
1.3
0.15
ns
1.6
1.5
I
Ijaji
1.8
LSD(0.05)
0.13
ns1.7
0.13
1.6
0.15
LSD(0.05)
ns
0.13
0.13
0.1
1.5
1.0
1.4
1.4
20002004
2001 2002 2003
20002004
2001 2002 2003 20
2000 2001 2002 2003
2000 2004
2001 2002 2003
Years
Years
Years
Years
1.7
LSD(0.05)
0.15
0.13
Gudar
LSD(0.05)
0.16
1.6 0.15
0.15 0.13
0.17
1.5
Total N (g kg-1)
Total N (g kg-1)
1.6
0.16
LSD(0.05)1.8
Gudar
1.7
LSD(0.05)
1.4
Tibe
Total N (g kg-1)
1.4
LSD(0.05)
0.15
0.16 0.151.3
Total N (g kg-1)
Total N (g kg-1)
1.3
1.6
LSD(0.05)
1.5
0.14
1.3
0.16
ns 0.15
ns 0.15
0.13
0.13
1.4
1.2
1.2
20002004
2001 2002 2003
20002004
2001 2002 2003 20
2000 2001 2002 2003
2000 2004
2001 2002 2003
Years
Years
Years
Years
1.5
Tibe
1.4
Shobo
Shoboka
Total N (g kg-1)
Bako
Total N (g kg-1)
3180
0.16
0.15
0.17
1.4
1.3
20002004
2001 2002 2003 2004
2000 2001 2002 2003
Years
Years
Figure 6. Effect of nitrogen fertilization on total N measured after harvesting in the 0-30 cm layer of Nitisols at the
five experimental sites in 2000 to 2004.
recommended N fertilization rate of 92 kg ha-1. An
increase of organic matter in these degraded soils should
improve their quality tremendously because Weil and
Magdoff (2004) stated that organic matter influences the
properties of mineral soils disproportionately to the
quantity of organic matter present. Soil of good quality
has according to Doran and Parkin (1994) the capacity to
sustain biological productivity, maintain environmental
Tolessa et al.
N (g kg-1)
1.3
1.5
1.8
1.0
Shoboka
N (g kg-1)
1.3
1.5
1.8
2.0
1.0
0.0
0.0
0.25
7.5
ns
7.5
ns
15.0
15.0
ns
MTRR
MTRV
CT
22.5
ns
ns
22.5
ns
30.0
30.0
1.0
N (g kg-1)
1.3
1.5
1.8
Tibe
2.0
1.0
0.0
1.3
7.5
0.27
15.0
LSD(0.05)
ns
22.5
Soil depth (cm)
Soil depth (cm)
N (g kg-1)
1.5
1.8
Ijaji
2.0
0.0
0.28
7.5
LSD(0.05)
0.20
0.21
15.0
0.15
22.5
ns
ns
30.0
30.0
1.0
Gudar
N (g kg-1)
1.3
1.5
1.8
2.0
MTRR = Minimum tillage with resdiues retained
0.0
LSD(0.05)
Soil depth (cm)
0.16
LSD(0.05)
Soil depth (cm)
LSD(0.05)
Soil depth (cm)
Bako
2.0
3181
0.21
7.5
0.13
MTRV =Minimum tillage with residues removed
CT
= Conventional tillage
15.0
ns
22.5
ns
30.0
Figure 7. Effect of tillage system on total N measured after harvesting in the 0 to 7.5 cm, 7.5 to 15 cm, 15 to 22.5 cm
and 22.5 to 30 cm layers of Nitisols at the five experimental sites in 2004.
quality and promote plant, animal and human health
which is essential for enhancing the sustainability of the
rural community in the region.
The initiation phase of MTRR when additional N
fertilization is usually needed seems favourably short
based on recorded grain yield (Tolessa et al., 2007) and
fertilization efficacy (Tolessa et al., 2009) over the 5
years trial period. During the final 2 years there was no
3182
Afr. J. Agric. Res.
7.5
ns
15.0
15.0
ns
22.5
ns
ns
Soil depth (cm)
Soil depth (cm)
ns
30.0
15.0
22.5
ns
ns
30.0
GudarN (g kg-1)
Gudar
N (g kg-1)
1.4 1.6 1.0 1.81.2 2.01.4 1.6 1.8 2.0
0.0
LSD(0.05)
nsLSD(0.05)
ns
7.5
ns
15.0
15.0
ns
ns
22.5
22.5
30.0
22.5
ns
1.8
1.2
Soil depth (cm)
1.0
7.5
Shoboka
1.8 2.0
30.0
30.0
0.0
ns
22.5
ns
15.0
Soil depth (cm)
Soil depth (cm)
0.0
Soil depth (cm)
1.2
IjajiN (g kg-1)
TibeN (g kg-1)
TibeN (g kg-1)
N (g kg-1)
1.4 1.6 1.0 1.81.2 2.01.4 1.61.0 1.81.2 2.01.4 1.6 1.0 1.8 1.2 2.0 1.4 1.6
0.0
0.0
0.0
0.20
LSD(0.05)
0.20
LSD
(0.05)
ns
ns
7.5
7.5 LSD(0.05)
7.5 LSD(0.05)
0.21
0.21
Soil depth (cm)
1.0
Soil depth (cm)
Soil depth (cm)
Soil depth (cm)
Bako
BakoN (g kg-1)
Shoboka
N (g kg-1)
N (g kg-1)
N (g kg-1)
1.0 1.2 1.4 1.6 1.0 1.81.2 2.01.4 1.61.0 1.81.2 2.01.4 1.6 1.0 1.8 1.2 2.0 1.4 1.6
0.0
0.0
0.0
0.0
LSD(0.05) 0.25
LSD(0.05) 0.25
LSD(0.05) ns
LSD(0.05) ns
7.5
7.5
7.5
7.5
ns
ns
ns
ns
15.0
15.0
15.0
ns
15.0
69
69 ns
ns
ns
92
22.5
92
22.5
ns
22.5 ns
22.5 ns115
115ns
30.0
30.0
30.0
30.0
ns
30.0
ns
ns
Figure 8. Effect of nitrogen fertilization on total N measured after harvesting in the 0 to 7.5 cm, 7.5 to 15 cm,
15 to 22.5 cm and 22.5 to 30 cm layers of Nitisols at the five experimental sites in 2004.
significant difference in grain yield between MTRV and
CT and both were significantly inferior to MTRR (Table
3). The agronomic and recovery efficient use of applied N
were also higher with MTRR than with either MTRV or
CT. In 2004, MTRR, MTRV and CT had an agronomic
-1
efficiency of 16.0, 11.8 and 9.4 kg grain kg N applied
and a recovery efficiency of 47.7, 34.8 and
33.2%,respectively.
The replacement of CT with MTRV is not an option at
all if the aim is to increase organic matter in the degraded
Nitisols of Western Ethiopia. This is because MTRV
exhausted the organic matter of these soils even more
than CT, probably due to the removal of the crop
residues.
We believe that the findings of this study could be
extrapolated to the remaining highland regions of
Ijaji
2.0
Tolessa et al.
3183
Table 3. Effect of tillage system [minimum tillage with residue retention (MTRR), minimum
tillage with residue removal (MTRV) and conventional tillage (CT)] on mean maize grain yield
of the five sites for each year.
Tillage system
MTRR
MTRV
CT
2000
7538a
7499a
6724b
Grain yield (kg ha-1)
2001
2002
2003
6249a
6307a
5898a
6545a
5787ab
4955b
b
b
5832
5410
5125b
2004
6374a
5577b
5752b
Means within a column followed by the same letter(s) are not significantly different at p = 0.05.
Ethiopia as well as those of other African countries like
Cameroon, Congo and Kenya. In these countries,
cropping on Nitisols is common, notably at altitudes of
more than 1200 m above sea level. The estimated area
of Nitisols in the highlands of Africa is 100 million ha.
Conclusions
Tillage systems and concomitant crop residue
management significantly affected organic C and total N.
This change in organic C and total N was confined to the
upper 0 to 7.5 cm layer. Both organic C and total N
increased steadily over the trial period with MTRR and
decreased with MTRV, and that of CT was intermediate
to MTRR and MTRV. Higher contents of organic C and
total N were recorded with higher fertilizer N applications
irrespective of tillage system. There was however over
time a steady (115 kg ha-1 N level) to decreasing (69 and
92 kg N levels) trend in organic C and total N. Based on
these results CT can be replaced with MTRR and in
addition proper fertilization, especially N is of utmost
importance to improve soil quality and secure sustainable
crop production in Western Ethiopia. These findings may
be applicable also to the remaining highland regions of
Ethiopia as well as those of other African countries where
cropping on Nitisols is common
Conflict of Interest
The authors have not declared any conflict of interest.
ACKNOWLEDGEMENTS
Financial support from International Maize and Wheat
Improvement Center (CIMMYT), Sasakawa Global 2000
and Ethiopian Agricultural Research Organization
(EARO) is gratefully acknowledged.
REFERENCES
Allison FE (1973). Soil organic matter and its role in crop production.
Elsevier, Amsterdam.
Baeumer K, Bakermans WAP (1973). Zero-tillage. Adv. Agron. 25:77123.
http://dx.doi.org/10.1016/S0065-2113(08)60779-8
Baker JM, Ochsner TE, Venterea RT, Griffis TJ (2007). Tillage and soil
carbon sequestration – What do we really know? Agric. Ecosyst.
Environ. 118:1-5.
http://dx.doi.org/10.1016/j.agee.2006.05.014
Bakht J, Shafi M, Jan MT, Shah Z (2009). Influence of crop residue
management, cropping system and N fertilizer on soil N and C
dynamics and sustainable wheat (Triticum aestivum L.) production.
Soil Tillage Res. 104:233-240.
http://dx.doi.org/10.1016/j.still.2009.02.006
Barber SA (1979). Corn residue management and soil organic matter.
Agron. J. 71:625-627.
http://dx.doi.org/10.2134/agronj1979.00021962007100040025x
Bezuayehu T, Gezahegn A, Yigezu A, Poulos D, Jabbar MA (2002).
Nature and causes of land degradation in the Oromiya region: A
review. Socio-economic and Policy Research Working paper No. 36,
ILRI, Addis Ababa.
Blevins RL, Thomas GW, Smith MS, Frye WW, Cornelius PL (1983).
Changes in soil properties after 10 years continuous non-tilled and
conventionally tilled corn. Soil Tillage Res. 3:135-146.
http://dx.doi.org/10.1016/0167-1987(83)90004-1
Bolliger A, Magid J, Amado TJC, Neto FS, Ribeiro MFS, Calegari A,
Ralisch R, De Neergaard A (2006). Taking stock of the Brazilian
"zero-till revolution": A review of landmark research and farmers'
practice. Adv. Agron. 91:47-110.
http://dx.doi.org/10.1016/S0065-2113(06)91002-5
Brady NC, Weil RR (2008). The nature and properties of soils, 14th edn.
Pearson Prentice Hall, Upper Saddle River, New Jersey.
Chaplot V, Mchunu CN, Manson A, Lorentz S, Jewitt G (2012). Water
erosion-induced CO2 emissions from tilled and no-tilled soils and
sediments. Agric. Ecosyst. Environ. 159:62-69.
http://dx.doi.org/10.1016/j.agee.2012.06.008
Doran JW, Parkin TB (1994). Defining and assessing soil quality. In:
Doran JW, Coleman DC, Bezdicek DF, Stewart BA (eds). Defining
soil quality for a sustainable environment. Soil Sci. Soc. Am.,
Madison, Wisconsin.
Du Preez CC, Van Huyssteen CW, Mnkeni PNS (2011). Land use and
organic matter in South Africa 2: a review on the influence of arable
crop production. S. Afr. J. Sci. 107:35-42.
http://dx.doi.org/10.4102/sajs.v107i5/6.358
http://dx.doi.org/10.4102/sajs.v107i5/6.354
Duiker SW, Beegle DB (2006). Soil fertility distributions in long-term notill, chisel/disk and mouldboard plow/disk systems. Soil Tillage Res.
88:30-41.
http://dx.doi.org/10.1016/j.still.2005.04.004
FAO (1998). World reference base for soil resources. World Soil
Resources Report No. 84, FAO, Rome.
Franzluebbers AJ (2004). Tillage and residue management effects on
soil organic matter. In: Magdoff F, Weil RR (eds.). Soil organic matter
in sustainable agriculture. CRC Press, New York.
http://dx.doi.org/10.1201/9780203496374.ch8
Griffith DR, Mannering JV, Box JE (1986). Soil and moisture
management with reduced tillage. In: Sprague MA, Triplett GB (eds.).
No-tillage and surface tillage agriculture. John Wiley & Sons, New
York.
Govaerts B, Verhulst N, Castellanos-Navarrete A, Sayre KD, Dixon J,
3184
Afr. J. Agric. Res.
Dendooven L (2009). Conservation agriculture and soil carbon
sequestration: between myth and farmer reality. CRC Crit. Rev. Plant
Sci. 28:97-122.
http://dx.doi.org/10.1080/07352680902776358
Hamblin AP, Tennant D (1979). Interactions between soil type and
tillage levels in a dry land situation. Aust. J. Soil Res. 17:177-189.
http://dx.doi.org/10.1071/SR9790177
Ismail I, Blevins RL, Frye WW (1994). Long-term no-tillage effects on
soil properties and continuous corn yields. Soil Sci. Soc. Am. J.
58:193-198.
http://dx.doi.org/10.2136/sssaj1994.03615995005800010028x
Kern JS, Johnson MG (1993). Conservation tillage impacts on national
soil and atmospheric carbon levels. Soil Sci. Soc. Am. J. 57:200-210.
http://dx.doi.org/10.2136/sssaj1993.03615995005700010036x
Lal R (1976). No-tillage effects on soil properties under different crops in
western Nigeria. Soil Sci. Soc. Am. J. 40: 762-768.
http://dx.doi.org/10.2136/sssaj1976.03615995004000050039x
Lal R (1993). Tillage effects on soil degradation, soil resilience, soil
quality, and sustainability. Soil Tillage Res. 27:1-8.
http://dx.doi.org/10.1016/0167-1987(93)90059-X
Larson WE, Clapp CE, Pierre WH, Morachan YB (1972). Effects of
increasing amounts of organic residues on continuous corn: 2.
Organic carbon, nitrogen, phosphorus and sulphur. Agron. J. 64:204208.
http://dx.doi.org/10.2134/agronj1972.00021962006400020023x
Loke PF, Kotze E, Du Preez CC (2012). Changes in soil organic matter
indices following 32 years of different wheat production management
practices in semi-arid South Africa. Nutr. Cycl. Agroecosyst. 94:97109.
http://dx.doi.org/10.1007/s10705-012-9529-6
Luo Z, Wang E, Sun OJ (2010). Can no-tillage stimulate carbon
sequestration in agricultural soils? A meta-analysis of paired
experiments. Agric. Ecosyst. Environ. 139:244-231.
http://dx.doi.org/10.1016/j.agee.2010.08.006
Mahboubi AA, Lal R, Faussey NR (1993). Twenty-eight years of tillage
effects on two soils in Ohio. Soil Sci. Soc. Am. J. 57:506-512.
http://dx.doi.org/10.2136/sssaj1993.03615995005700020034x
Michigan State University (1989). MSTAC. A microcomputer for the
design, management and analysis of agronomic research
experiments. Michigan State University, East Lansing, United States
of America.
Phillips RE, Blevins RL, Thomas GW, Frye WW, Phillips SH (1980). Notillage agriculture. Sci. 208: 1108-1113.
http://dx.doi.org/10.1126/science.208.4448.1108
PMid:17783055
Phillips SM, Young HM (1973). No-tillage farming. Resmain Associates,
Milwaukee, Wisconsin.
Powlson D, Smith P, De Nobili M (2013). Soil organic matter. In:
Gregory PJ, Northcliff S (eds.). Soil conditions and plant growth.
Wiley-Blackwell, Oxford.
Rasmussen PE, Collins HP (1991). Long-term impacts of tillage,
fertilizer and crop residue on soil organic matter in temperate semiarid regions. Adv. Agron. 45:93-134.
http://dx.doi.org/10.1016/S0065-2113(08)60039-5
Reicosky DC, Kemper WD, Langdale GW, Douglas CW, Rasmussen
PE (1995). Soil organic matter changes resulting from tillage and
biomass production. J. Soil Water. Conserv. 50:253-261.
Rice CW, Smith MS, Blevins RL (1986). Soil nitrogen availability after
long-term continuous no-tillage and conventional tillage corn
production. Soil Sci. Soc. Am. J. 50:1206-1210.
http://dx.doi.org/10.2136/sssaj1986.03615995005000050023x
Smith JL, Elliot LF (1990). Tillage and crop residue management effects
on soil organic matter dynamics in semi-arid regions. Adv. Soil Sci.
13:69-88.
http://dx.doi.org/10.1007/978-1-4613-8982-8_4
Stevenson FJ, Cole MA (1999). Cycles in soil: carbon, nitrogen,
phosphorus, sulphur and micronutrients, 2nd edn. John Wiley &
Sons, New York.
The Non-Affiliated Soil Analysis Work Committee (1990). Handbook of
standard soil testing methods for advising purposes. Soil Science
Society of South Africa, Pretoria.
Tolessa D, Du Preez CC, Ceronio GM (2007). Effect of tillage system
and nitrogen fertilization on yield and yield components of maize in
Western Ethiopia. S. Afr. J. Plant Soil 24:63-69.
http://dx.doi.org/10.1080/02571862.2007.10634783
Tolessa D, Du Preez CC, Ceronio GM (2009). Effect of tillage system
and nitrogen fertilization on efficacy of applied nitrogen by maize in
Western Ethiopia. S. Afr. J. Plant Soil 26:36-44.
http://dx.doi.org/10.1080/02571862.2009.10639930
Tolessa D, Tesfa B, Wakene N, Tenaw W, Minale L, Tewodros M,
Burtukan M, Waga M (2002). A review of fertilizer management
research on maize in Ethiopia. In: Mandefiro N, Tanner D, TwumasiAfriyie S (eds.). Proceedings of the Second Maize Workshop of
Ethiopia. Addis Ababa.
Triplett GB, Van Doren DM (1969). Nitrogen, phosphorus and
potassium fertilization for non-tilled maize. Agron. J. 61:637-639.
http://dx.doi.org/10.2134/agronj1969.00021962006100040047x
Triplett GB, Van Doren DM (1977). Agriculture without tillage. Sci. Am.
236:28-33.
http://dx.doi.org/10.1038/scientificamerican0177-28
Unger PW (1991). Organic matter, nutrient, and pH distribution in noand conventional-tillage semiarid soils. Agron. J. 83:186-189.
http://dx.doi.org/10.2134/agronj1991.00021962008300010044x
Weil RR, Magdoff F (2004). Significance of soil organic matter to soil
quality and health. In: Magdoff F, Weil RR (eds.). Soil organic matter
in sustainable agriculture. CRC Press, New York.
http://dx.doi.org/10.1201/9780203496374.ch1
White PF (1990). The influence of alternative tillage systems on the
distribution of nutrients and organic carbon in some common western
Australian wheatbelt soil. Aust. J. Soil Res. 28:95-116.
http://dx.doi.org/10.1071/SR9900095
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